Master Thesis Geoecology _Appendix - Ökologisch

Transcription

Variation and Distribution of Forest Types
on the Southern Foothills of the Cordillera Cahuapanas,
Alto Mayo, Peru
Diplomarbeit
Geoecology
by
Johannes Dietz
Bayreuth, Germany, June 2002
There is a theory which states that if ever anybody discovers
exactly what the Universe is for and why it is here, it will instantly
disappear and be replaced by something even more bizarre and
inexplicable. There is another theory which states that this has
already happened ...
DOUGLAS ADAMS
Acknowledgements
For successfully realizing this work I owe my sincere gratitude to…
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Prof. Dr. Klaus Müller-Hohenstein for providing the research topic and constructive support,
Dr. Reiner Zimmermann for his equipment, his positive spirit and creative attitude, his guidance and fruitful
discussions, his patience and his trust. I consider it a privilege to have enjoyed his collaboration in an
exceptionally pleasant and productive working atmosphere.
Ing Arno Perisutti for his interest, and his support during the project preparation and field work in Peru,
PD Dr. Gregor Aas for providing me with a comfortable work space at the Ecological Botanical Garden (ÖBG) in
Bayreuth,
Annett Börner who contributed substantially to the success of this study. Her preparation, her tireless moral as well as
her qualified scientific support were invaluable during the entire period of my work,
Jan Dempewolf, Tobias Mette and Holger Treidel of the Forest Ecology and Remote Sensing Group (F.E.A.R.S.) at
the Ecological Botanical Garden, Univerisität Bayreuth, who have provided me with a solid basis through their
intensive preparatory and supplementary work,
Dr. Marianne Lauerer, Dr. Viviana Horna de Zimmermann, Dr. Raymond Schwartz, Catharine Copass, Thomas
Nehls, Martin Endres, Isabel Seifert and Steffen Dietz for their critical reviews, valuable advice and
discussion of my manuscript,
Dr. Johannes Refisch and Ulrich Sukopp for suggestions regarding the statistical analysis,
PD Dr. Wolfgang Wilcke and Dr. Syafrimen Yasin for their discussions in soil evaluation,
Dr. Gunter Ilgen and Petra Dietrich of the Zentrale Analytik laboratory at the Bayreuther Institut für terrestrische
Ökosystemforschung (BITÖK) and Margarete Wartinger for the analysis of my soil samples,
Dr. Andreas Peterek for technical support in spatial analysis,
Rosemarie Mendler (Friedrich-Schiller-Universität, Jena) for her assistance in battling the odds while digitizing the
elevation map,
Juan Ruiz Castro for his endless energy, his machete, his universal outdoor skills and his excellent talent for
organizing all field work which was essential to the success of this mission,
Henry Soplín Roque, Alexander Reátegui Reátegui, and Jose Pezo Seijas of the Universidad de San Martín,
Facultad de Ecología, Moyobamba, for their uncompromising support under all field conditions. Their energy
and humor permitted the collection of all data and a pleasant working atmosphere in the field,
Pablo Contreras Vázquez for his 4x4 driving skills. He guaranteed safe and reliable transport during my stay in Peru,
Patricia Casique Celis, Guisella Arévalo Del Aguila and all other staff members of GTZ-DIAM and PEAM for their
patience and unbureaucratic support. They always provided a safe haven and logistic backup in Moyobamba.
Jasivia Gonzales Rocabado (Alfred-Haller Institut, Göttingen), Delicia Pino Garay (Universidad Nacional Agraria La
Molina, Lima), Luis Valenzuela and Alexander Monteagudo (Universidad Nacional Antonio Abad, Cusco),
and Dr. Winfried Meier (Universität Freiburg) for their expertise during the identification of my plant
specimen,
the Gesellschaft für Technische Zusammenarbeit (GTZ-DIAM) for supporting this project with a stipend,
the Proyecto Especial Alto Mayo (PEAM) for their cooperation especially through J. Castro and H. Soplín,
the Max-Planck Institute for Biogeochemistry in Jena and the Universidad Nacional de San Martín, Facultad de
Ecología in Moyobamba, for logistical support,
the people of the Comunidades Nativas, who permitted my investigation on their territory,
Jorge Toro and his family who provided comfortable accommodation and delicious country cuisine during many nights
in the field,
the people of Moyobamba, Gamínedes and Paz y Esperanza who always received and cared for me cordially,
Julio and Julia Horna for their true hospitality and assistance during my time in Lima,
Constanze Schaaff, Andreas von Heßberg and all my friends for their revitalizing support and their patience during
long office hours,
Microsoft™ for providing me with the pleasure of innumerable, futile extra computer hours, with sleepless nights and
an occasionally alarming surge in blood pressure in connection with destructive reviews of my manuscript,
… and ultimately to my parents Elfriede and Werner who have supported me and believed in me all my life.
I thank them for teaching me the inquisitiveness to the small and neglected and for understanding my urge to
venture to new exotic lands.
Variation and Distribution of Forest Types on the Southern Foothills of the Cordillera Cahuapanas, Alto Mayo, Peru
ABSTRACT
The aim of this study was the structural and edaphic classification of primary forests in
the upper watershed of the Río Avisado, Alto Mayo Valley, in Northern Peru. Special
emphasis was placed on correlations between abiotic environmental factors and the
occurrence of distinct vegetation types. The results of this study are intended as a basis for
an ecological sensitivity assessment of the area and as a guideline for the development of
an integrated management and protection plan for the region.
Low productivity semi-deciduous forests of the studied area are found on a highly dissected peneplain
in the upper Rio Avisado watershed (900 - 1 050 m a.s.l.) and on the southern piedmonts of the Cordillera
Cahuapanas (1 000 - 1 150 m a.s.l.) under tropical moist conditions. The highest elevation stands of lowstatured heath forests reported in tropical America occur at approx. 1 000 m a.s.l..
Forty-five field plots were chosen in order to characterize all structurally distinguishable forest stands
and their geology and topography. In these plots, soils were sampled and the stand structure and
dimensions of all woody plants were assessed. The woody above-ground biomass was harvested on ten
low-statured heath and on six fern plots.
In order to identify key abiotic factors and to distinguish statistically the different forest types
parameter from all sites were analyzed by principal component analysis and hierarchical cluster analysis.
This was done separately for soil chemistry, soil texture, biomass distribution, and vegetation structure
parameters. For this purpose, soils were analyzed chemically by top and subsoil layers. Above-ground
biomass was estimated on the basis of tree dimensions and biomass harvests.
The analysis of physical and chemical soil parameters showed that the white sand soils of the heath
forests provide the most adverse edaphic conditions to plant growth due to their high acidity and lack of
plant-available nutrients in the mineral soil. “Shapumbales” were associated with thick litter layers over
different substrates. Soils rich in base cations were mostly associated with stagnant water and thus were
likely to be limited by insufficient nitrogen mineralization. All other soils were deficient in phosphorus. A
restriction of plant growth through aluminum toxicity was not observed.
The analysis of tree perimeters and heights resulted in the separation of five stand types. With respect
to the accumulated above-ground biomass, however, they could only be distinguished significantly into
“poor” (< 50 t ha-1), “moderate” (± 100 t ha-1), and “rich” stands (> 200 t ha-1).
Statistical analysis of the vegetation resulted in eight forest stand types. A classification scheme for the
distinction of these forest types was developed based on selected stand structural and topographic
parameters, i. e. stand height and density, canopy cover, leaf morphology, palm proportions, and slope. On
white sand plateaus two open and stunted vegetation types were classified as heath forests (“chamizales”).
The forest type “shapumbal” (open forest on steep slopes with dense undergrowth of Sticherus remotus)
was assumed to be a successional stage on naturally disturbed sites. “Palm rich rainforests” dominated by
the species Jessenia bataua occurred in ravines and in narrow valleys. Wide valleys supported
“premontane rainforests” which were the only stands exceeding 25 m in height. Along upslope gradients
the forest types “low canopy rainforest”, “dry rainforest”, and “impoverished rainforest” occurred which
decrease in tree height but increase in stand density.
A classification of the forest stands in the study area was achieved and a link was established between
the geologic and topographic conditions that in turn influence drainage and the variability of soils. Biomass
was correlated most positively to the availability of phosphorus, magnesium and potassium in the soil and
to parameters indicating rapid nitrogen mineralization. This correlation of vegetation types with abiotic
factors provides the base for a subsequent mapping of forest types in the study area.
Site conditions which limit the potential of the studied area for forestry or agricultural use were
identified. These risks are soil erosion and land slides on slopes of the upper hill area and limited plant
growth on strongly acidic and nutrient poor soils. This study of the edaphic conditions in the Río Avisado
area provides a base for further investigation on erosion hazards, water and nutrient balances, and the
floristic characterization of the specific vegetation units.
Variación y Distribución de los Tipos de Bosque en los Piedemontes al Sur de la Cordillera Cahuapanas, Alto Mayo, Perú
RESUMEN
El objetivo del presente trabajo fué el de obtener una clasificación estructural y edáfica de los
bosques primarios de la cuenca alta del Río Avisado, valle del Alto Mayo, en el norte del Perú. Se dió
énfasis especial a las correlaciones entre los factores abióticos ambientales y la presencia de diferentes
tipos de vegetación. Los resultados del presente estudio sirven de base para una evaluación de
sensitividad ecológica del área trabajada y como una guía para el desarrollo de un plan de manejo
integrado para la región.
En el área de estudio bosques semi deciduos de baja productividad son encontrados en las regiones
fuertemente disectadas de las llanuras de la cuenca del Río Avisado (900 - 1 050 m s.n.m.) y en la parte de sur de
los piedmontes de la Cordillera Cahuapanas (1 000 - 1 150 m s.n.m.) bajo condiciones tropicales húmedas. A una
altitud aproximada de 1 000 m s.n.m. se encuentran poblaciones de bosque de heath de baja estatura, esta es la
mayor elevación registrada para este tipo de bosque en toda América tropical.
Se escogieron 45 parcelas con el fin de caracterizar diferentes tipos estructurales de bosque con relación a
condiciones de geología y topografía. Se muestraron los suelos y se evaluó la estructura del bosque y las
dimensiones de todas las plantas leñosas. Se colectó la biomasa leñosa aerea de diez parcelas de bosque de heath
y de seis parcelas de helechos.
Con el fin de identificar factores abióticos clave y para distinguir estadistícamente, los diferentes tipos de
bosque, se hizó un análisis de componentes principales y un análisis jerárquico de clusters de los parámetros de
todos los sitios de muestreo. Estos análises fueron hechos en forma separada para los parámetros de
características químicas y textura de suelos, distribución de biomasa, y estructura de la vegetación. También, con
este propósito, para el análisis químico de los suelos se consideró el suelo superficial y el sub-suelo, y se estimó
la cantidad de biomasa aérea a partir de las dimensiones de los árboles y de los datos de la biomasa cosechada.
El análisis de las características físicas y químicas de los suelos mostraron que los suelos de arenas blancas
del bosque de heath proporcionan las condiciones edáficas más adversas para el crecimiento de la vegetación,
debido a su alto grado de acidez y a la escasez de nutrientes disponibles para las plantas en el suelo mineral. Los
“shapumbales” se encontraron asociados a capas profundas de hojarasca sobre diferentes tipos de substrato. Los
suelos con un alto nivel de cationes se encontraron asociados a malas condiciones de drenaje y por lo tanto
fueron más bien limitados por los bajos niveles de mineralizacion de nitrógeno.Todos los otros suelos
presentaron bajos níveles de fósforo. Sin embargo, no se observó una limitación al crecimiento de las plantas
debido a niveles tóxicos de aluminio.
Como resultado del análisis de alturas y diámetros de los árboles medidos se obtuvó una separación entre
cinco grupos estructurales. Con respecto a la cantidad de biomasa aérea acumulada, sólo se distinguieron tres
tipos de parcelas: “pobres” (< 50 t ha-1), “moderadas” (± 100 t ha-1), y “ricas” (> 200 t ha-1).
El análisis estadístico de la vegetación dió como resultado ocho tipos de bosque. Se desarrolló un esquema de
clasificación de los tipos de bosque basado en características escogidas de la estructura de las parcelas de bosque
y de los parámetros topográficos(altura y densidad de los árboles en la parcela, cobertura del dosel, morfología
foliar, ocurrencia de palmeras y nível de pendientes). Se observaron dos tipos de vegetación abierta y truncada
que se clasificaron como bosque de heath (“chamizales”) en las mesetas de arenas blancas. El tipo de bosque
“shapumbal” (bosque abierto en pendientes fuertes con un sotobosque denso de Sticherus remotus) era un
estadío sucesional en áreas naturalmente disturbadas. Los bosques lluviosos con alta proporción de palmeras
(Palm rich rainforests) dominados por la especie Jessenia bataua se encuentran en barrancos y valles angostos.
Los valles anchos están cubiertos por bosque premontano que está representado por parcelas de más de 25 m de
altura. A lo largo de los gradientes de pendientes se encuentran los tipos de vegetación bosque lluvioso bajo (low
canopy rainforest), bosque lluvioso seco (dry rainforest), y el bosque lluvioso empobrecido (impoverished
rainforest). Estos boques muestran una disminución en altura de los árboles y un incremento en la densidad de
las parcelas.
Se logró obtener una buena clasificación de las parcelas forestales en el área de estudio, así como también se
pudó establecer una conección con las condiciones geológicas y de topografía, que a su vez influencian el patrón
de drenaje y la variabilidad entre diferentes tipos de suelo. Se correlacionó de forma positiva el valor estimado
de biomasa con los contenidos de fósforo, magnesio, potasio, y los niveles de mineralización rápida de
nitrógeno. La correlación entre los diferentes tipos de vegetación y los factores abióticos proporcionan una base
para el subsequente mapeo de los tipos de bosque en el área de estudio
Se identificaron las condiciones locales que limitan el uso potencial del área de estudio con fines agrícolas y
forestales. Estas son el riesgo de erosión de los suelos y de deslizamientos en zonas de pendiente en la parte alta
de la región colinosa y el reducido crecimiento de plantas en suelos fuertemente ácidos y de bajo contenido de
nutrientes. Los resultados sobre las condiciones edáficas en el área del Río Avisado deben servir como base para
planear futuras investigaciones sobre los peligros de erosión, balance de nutrientes y agua, y la composición
florística de los tipos de bosque identificados en la región.
Variation und Verteilung von Waldtypen im südlichen Vorland der Cordillera Cahuapanas, Alto Mayo, Peru
ZUSAMMENFASSUNG
Ziel der Untersuchung war die strukturelle und standörtliche Klassifikation von
Primärwäldern am Oberlauf des Río Avisado in der Region Alto Mayo, Nordperu. Im
Mittelpunkt der Arbeit stand dabei die Erforschung von Zusammenhängen zwischen
abiotischen Umweltfaktoren und dem Auftreten bestimmter Vegetationstypen. Die Ergebnisse
dieser Studie sollen die Grundlage bilden für die Bewertung der ökologischen Sensibilität des
Gebietes und für die Erarbeitung eines integrativen Management- und Schutzplans der Region.
Im Untersuchungsgebiet finden sich magere, teillaubwerfende Wälder auf einer stark zerklüfteten
Rumpffläche im oberen Einzugsgebiet des Río Avisado (900 - 1050 m ü. NN) und am Fuß der Cordillera
Cahuapanas (1000 - 1150 m ü. NN) unter feuchttropischem Klima. Auf ca. 1000 m ü. NN liegen die
höchstgelegenen niederwüchsigen Heidewälder, die im tropischen Amerika bekannt sind.
Fünfundvierzig Untersuchungsplots wurden ausgewählt, um die vorkommenden Waldbestände auch
nach geologischen und topografischen Gesichtspunkten charakterisieren zu können. Auf diesen Plots
wurden die Böden beprobt und die Bestandesstruktur und die Dimensionen der verholzenden Pflanzen
erhoben. Auf zehn Plots mit niederwüchsigem Heidewald und sechs Plots mit dominierender FarnVegetation wurde die oberirdische Biomasse verholzender Pflanzen geerntet.
Um wichtige abiotische Einflussfaktoren zu identifizieren und verschiedene Waldtypen statistisch
signifikant auszuweisen, wurden die Daten aller Plots durch Hauptkomponentenanalyse und hierarchische
Clusteranalyse ausgewertet. Dies geschah getrennt nach Parametern zu Bodenchemismus und Textur,
Biomasseverteilung und Vegetationsstruktur. Die Böden wurden dazu chemisch analysiert und die
oberirdische Biomasse wurde anhand von Baumdimensionen und Biomasseernten abgeschätzt.
Die Analyse der physikalischen und chemischen Bodenparameter wies die weißen, stark sauren
Sandböden der Heidewälder, die praktisch keine pflanzenverfügbare Nährstoffe im Mineralboden besitzen,
als extreme Mangelstandorte aus. „Shapumbales“ waren verbunden mit mächtigen Streulagen über
verschiedenen Substraten. Basenreiche Böden standen meist unter Stauwassereinfluss und Pflanzenwuchs
war daher wahrscheinlich durch unzureichende Stickstoffmineralisation limitiert. In allen anderen Böden
war die Phosphorversorgung mangelhaft. Eine Beeinträchtigung des Pflanzenwachstums durch
Aluminiumtoxizität konnte allerdings nicht festgestellt werden.
Die Analyse von Stammumfängen und Baumhöhen ergab eine Unterscheidung der Bestände in fünf
Gruppen. Bezüglich der gebildeten oberirdischen Biomasse konnten diese Bestände statistisch signifikant
allerdings nur in „arm“ (< 50 t ha-1), „mäßig“ (± 100 t ha-1), und „reich“ (> 200 t ha-1) eingeteilt werden.
Die statistische Analyse der Vegetation ergab acht Waldtypen. Anhand ausgewählter Parameter zu
Bestandesstruktur und Topografie (Bestandeshöhe und -dichte, Kronendeckung, Blattmorphologie,
Palmenanteil und Hangneigung) wurde ein Klassifikationsschema zur Unterscheidung dieser Waldtypen
entwickelt. Auf Plateaus aus weißen Sanden wurden zwei offene, niederwüchsige Vegetationstypen als
Heidewälder klassifiziert („Chamizales“). Der Waldtyp „Shapumbal“, ein offener Wald in Steillagen mit
dichtem Unterwuchs aus Sticherus remotus, scheint ein Sukzessionsstadium auf natürlich gestörten
Standorten zu sein. „Palmenreiche Regenwälder“ kamen in Schluchten und engen Tälern vor und waren
dominiert von Jessenia bataua. Weite Täler enthielten „submontane Regenwälder“, die als einzige
Bestände eine Höhe von über 25 m erreichten. Entlang eines hangaufwärts gerichteten Gradienten standen
„niedriger Regenwald“, „trockener Regenwald“ und „verarmter Regenwald“ bei abnehmender Baumhöhe
und steigender Bestandesdichte.
Eine Klassifikation der Waldbestände wurde erfolgreich durchgeführt und in Zusammenhang gestellt
mit edafischen und topografischen Faktoren, die wiederum den Wasserhaushalt und die Variabilität der
Böden beeinflussen. Die Biomasse korrelierte positiv am besten mit der Verfügbarkeit von Phosphor,
Magnesium und Kalium im Boden sowie mit Parametern, die auf eine rasche Stickstoffmineralisierung
hinweisen. Diese Korrelation von Vegetationseinheiten mit abiotischen Faktoren bildet die Grundlage für
eine anschließende Kartierung der Waldtypen im Untersuchungsgebiet.
Es wurden die Standortbedingungen beschrieben, die eine wald- und ackerbauliche Nutzung des
Untersuchungsgebiets einschränken. Dazu zählt die Gefahr von Bodenerosion und Hangrutschungen im
oberen Hügellande sowie limitiertes Pflanzenwachstum auf stark sauren, nährstoffarmen Böden. Die
Ergebnisse der vorliegenden Studie zu den edafischen Bedingungen im Río Avisado Gebiet ermöglichen es
nun, weitere Untersuchungen zu Erosionsrisiken, zum Wasser- und Nährstoffhaushalt sowie eine
floristische Charakterisierung der ausgeschiedenen Vegetationseinheiten zu planen.
I
Table of Contents
Contents
Contents
Table of Contents
A B S T R AC T . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
RESUMEN ..............................................................
ZUSAMMENF ASSUNG .............................................
C O N TE N TS
Table of Contents ........................................................................................................I
Table of Figures ........................................................................................................ III
Table of Tables........................................................................................................... V
..................................................................1
1 INT RODU CT ION
2 M AT E R I AL S A N D M E T H O D S . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6
2.1 Study Area ........................................................................................................... 6
2.1.1
2.1.2
2.1.3
2.1.4
2.1.5
2.1.6
Location and History ......................................................................6
Climate.............................................................................................9
Geology and Geomorphology......................................................11
Soils...............................................................................................14
Vegetation .....................................................................................15
Photographic Survey of the Study Region..................................16
2.2 Field Data ........................................................................................................... 18
2.2.1
Plot selection ................................................................................18
2.2.2
Recorded Parameters...................................................................19
2.2.2.1
Site Characteristics .....................................................................19
2.2.2.2
Soils ............................................................................................20
2.2.2.3
Vegetation Structure....................................................................21
2.2.2.4
Biometry......................................................................................23
2.3 Data Processing and Analysis ...................................................................... 24
2.3.1
2.3.1.1
2.3.1.2
2.3.1.3
2.3.2
2.3.2.1
2.3.2.2
2.3.2.3
2.3.2.4
2.3.3
2.3.3.1
2.3.3.2
2.3.4
Data Processing............................................................................24
Soils ............................................................................................24
Vegetation Structure....................................................................25
Biometry......................................................................................26
Statistical Analysis .......................................................................29
Spearman’s Correlation Coefficient .............................................29
Principal Component Analysis.....................................................29
Hierarchical Cluster Analysis.......................................................30
One-Way ANOVA Analysis .........................................................31
Image Processing .........................................................................32
Aerial Photography......................................................................32
Digital Elevation Model Processing .............................................32
Software ........................................................................................33
Table of Contents
3 RESULT S
Contents
II
............................................................ 34
3.1 Soils of the Study Area ................................................................................... 34
3.1.1
Statistical Classification...............................................................34
3.1.2
Results of Soil Analysis ...............................................................37
3.1.3
Description ....................................................................................42
3.1.3.1
Soil Units.....................................................................................42
3.1.3.2
Summary of Soil Analysis............................................................44
3.1.3.3
Statistical Evaluation ...................................................................46
3.2 Stand Biometrical Analysis ............................................................................ 48
3.2.1
3.2.2
Results of Biometrical Analysis...................................................50
3.2.3
Description ....................................................................................54
3.2.3.1
Biometry Units.............................................................................54
3.2.3.2
Summary.....................................................................................56
3.2.3.3
3.3 Combined Approach..................................................................................59
3.3.1
3.3.2
Results of Combined Analysis ....................................................63
3.3.2.1
Soil..............................................................................................63
3.3.2.2
Biometry......................................................................................67
3.3.2.3
Structure .....................................................................................70
3.3.3
Description ....................................................................................72
3.3.3.1
Vegetation Units..........................................................................72
3.3.3.2
Summary.....................................................................................74
3.3.3.3
3.4 Spatial Distribution of Vegetation................................................................. 77
3.4.1
3.4.2
3.4.3
3.4.4
3.4.4.1
3.4.4.2
4 DISC US S IO N
Image Data ....................................................................................77
Topographic Data .........................................................................77
Spatial Arrangement and Analysis ..............................................78
Proposed Mapping Criteria ..........................................................83
Vegetation Types ........................................................................83
Biomass ......................................................................................84
................................................................ 85
4.1 Classification Approaches ............................................................................. 85
4.1.1
4.1.2
4.1.3
4.1.4
Soil Chemistry and Texture..........................................................86
Above-Ground Biomass...............................................................89
Topography...................................................................................91
Simplified Classification ............................................................101
4.2 Ecological Evaluation.................................................................................... 103
4.3 Outlook ............................................................................................................. 104
5 S U M M AR Y
.............................................................. 105
6 L I T E R AT U R E
........................................................... 107
7 GLOSSARY
........................................................... 112
8 A B B R E V I AT I O N S . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1 1 4
.............................................................. 116
9 APPENDIX
III
Contents
Table of Figures
Table of Figures
Fig. 2.1: Location of the study site indicated on an edited NOAA-AVHRR satellite image of Peru......................... 6
Fig. 2.2: Climate diagram of Rioja, Alto Mayo, North Central Peru. ..................................................................... 9
Fig. 2.3: Monthly sum of precipitation for two meteorological stations in the Alto Mayo Valley, Northern Peru......10
Fig. 2.4: Comparison of El Niño events to the precipitation situation in the Alto Mayo Valley. ..............................10
Fig. 2.5: Geological map of the upper Río Tioyacu and Río Avisado watersheds................................................12
Fig. 2.6: Radar image of the upper Río Tioyacu and Río Avisado watersheds. ...................................................13
Fig. 2.7: Location of the study site highlighted on a section of the forest type map of Peru..................................15
Fig. 2.8: View to the south east from the Cerro Tambo into the Alto Mayo Valley................................................16
Fig. 2.9: Abandoned agricultural clearing at the foot of the Cordillera Cahuapanas near the Río Huascayaquillo. 16
Fig. 2.10: Recently burnt vegetation on a ridge east of the Río Avisado showing clear signs of onsetting erosion.
........................................................................................................................................................16
Fig. 2.11: Panoramic view across the study area towards the ENE from a pre-summit of the Cerro Tambo. ........17
Fig. 2.12: Panoramic view to the ESE of a recently cleared patch of land on a gentle slope in the lower hill area.17
Fig. 2.13: Panoramic view over extended rice fields on the bottom of the Alto Mayo Valley north of Valle de la
Conquista. ......................................................................................................................................17
Fig. 2.14: Comparison of tree height yields from dbh measurements using three different equations by OGAWA
plotted against actual tree heights derived from field measurements.................................................27
Fig. 2.15: Tree height yields from dbh measurements using three different equations by OGAWA plotted against
actual tree heights. .........................................................................................................................27
Fig. 2.16: Hypothetical curve of the number of clusters plotted against the employed distance measure. ............31
Fig. 3.1: Soil analysis: Number of clusters plotted against the employed distance measure: squared Euclidean
distance. ............................................................................................................................................35
Fig. 3.2: Dendrogram of soil clusters generated by Hierarchical Cluster Analysis using Ward’s Method...............36
Fig. 3.3: Topsoil texture composition of the fractions sand, silt and clay of the soil units......................................37
Fig. 3.4: Subsoil texture composition of the fractions sand, silt and clay of the soil units. ....................................37
Fig. 3.5: The pH values of the soil units. ...........................................................................................................38
Fig. 3.6: Distribution of C/N ratio in the soil units. ..............................................................................................38
Fig. 3.7: Distribution of Ctot concentration in the soil units. .................................................................................39
Fig. 3.8: Distribution of Ctot pools in the soil units...............................................................................................39
Fig. 3.9: Distribution of Ntot concentration in the soil units. .................................................................................39
Fig. 3.10: Distribution of Ntot pools in the soil units.............................................................................................39
Fig. 3.11: Distribution of exchangeable K+ concentration in the soil units............................................................40
+
Fig. 3.12: Distribution of exchangeable K pools in the soil units. .......................................................................40
Fig. 3.13: Distribution of plant-available POlsen pools in the topsoil of the soil units. .............................................40
Fig. 3.14: Distribution of exchangeable Al3+ concentration in the soil units..........................................................40
Fig. 3.15: Topsoil contribution of the exchangeable portion of bases to the effective cation exchange capacity....41
Fig. 3.16: Subsoil contribution of the exchangeable portion of bases to the effective cation exchange capacity. ..41
Fig. 3.17: Relation between soil chemistry and biomass for the respective soil units...........................................46
Fig 3.18: Number of clusters plotted against the squared Euclidean distance. ....................................................49
Fig 3.19: Dendrogramm generated by Hierarchical Cluster Analysis using Ward’s method for the extraction of
clusters
with maximum similarity. .................................................................................................49
Fig. 3.20: Means of non-tree woody individuals and standing dead wood for each biometric unit. .......................51
Fig. 3.21: Growth density means plotted for each biometric unit with standard deviations. ..................................51
Fig. 3.22: Regression fitted to the diameter at breast height vs. tree height plot for all assessed trees.................52
Fig. 3.23: Regressions fitted to the diameter at breast height vs. tree height plots for each biometric unit............52
Fig. 3.24: Girth at breast height means plotted for each biometric unit with standard deviations. .........................53
Fig. 3.25: Tree height means plotted for each biometric unit with standard deviations.........................................53
Fig. 3.26: Distribution of stems into basal area classes for the classified biometric units. ....................................53
Fig. 3.27: Distribution of stems into tree height classes for the classified biometric units. ....................................53
Fig. 3.28: Basal area means with standard deviations of the biometric units.......................................................54
Fig. 3.29: Standing above ground biomass means with standard deviations of the biometric units ......................54
Fig. 3.30: Relation between soil chemistry and biomass for the respective biometry units...................................57
Fig. 3.31: Combined analysis: Number of clusters plotted against the employed distance measure: squared
Euclidean distance. ......................................................................................................................... 60
Table of Figures
Contents
IV
Fig. 3.32: Dendrogram generated by Hierarchical Cluster Analysis using Ward’s Method. ................................. 61
Fig. 3.33: Topsoil texture composition of the fractions sand, silt, and clay of the classified units. ........................ 63
Fig. 3.34: Subsoil texture composition of the fractions sand, silt, and clay of the classified units. ........................ 63
Fig. 3.35: The pH values in the classified units. ................................................................................................ 64
Fig. 3.36: Distribution of C/N ratio in the classified units of the study area.......................................................... 64
Fig. 3.37: Distribution of Ctot concentration in the respective classified units. ..................................................... 64
Fig. 3.38: Distribution of Ctot pools in the respective classified units................................................................... 64
Fig. 3.39: Distribution of exchangeable aluminum concentration. ...................................................................... 65
Fig. 3.40: Distribution of POlsen pools in the topsoil. ........................................................................................... 65
Fig. 3.41: Topsoil contribution of the exchangeable portion of bases to the effective cation exchange capacity... 65
Fig. 3.42: Subsoil contribution of the exchangeable portion of bases to the effective cation exchange capacity... 65
Fig. 3.43: Density of non-tree woody individuals and standing dead wood for each classified unit. ..................... 67
Fig. 3.44: Growth density means plotted for each classified unit with standard deviations .................................. 67
Fig. 3.45: Girth at breast height means plotted for each classified unit with standard deviations. ........................ 67
Fig. 3.46: Tree height means plotted for each classified unit with standard deviations........................................ 67
Fig. 3.47: Basal area means plotted for each classified unit with standard deviations......................................... 68
Fig. 3.48: Standing above ground biomass means plotted for each classified unit with standard deviations. ....... 68
Fig. 3.49: Proportional leaf size distribution in the lower tree layer..................................................................... 70
Fig. 3.50: Proportional leaf size distribution in the medium tree layer................................................................. 70
Fig. 3.51: Cumulative canopy height chart........................................................................................................ 71
Fig. 3.52: Caniopy cover by each individual layer. ............................................................................................ 71
Fig. 3.53: Relation between soil chemistry and biomass for the respective biometry units. ................................. 76
Fig. 3.54: JERS-1 radar image of the study area showing coarse spatial resolution and intense foreshortening.. 78
Fig. 3.55: LANDSAT image of the study area showing high cloud cover. .............................................................. 78
Fig. 3.56: Topographic map of the study area showing low vertical resolution.................................................... 78
Fig. 3.57: Panchromatic aerial photograph of the study area............................................................................. 78
Fig. 3.58: Spatial distribution of vegetation types as classified by combined analysis. ........................................ 79
Fig. 3.59: Panoramic view across the upper hill area towards the East from a pre-summit of the Cerro Tambo... 81
Fig. 3.60: Panoramic view across the study area towards the Cordillera Cahuapanas in the Río Avisado area. .. 81
Fig. 3.61: Black and white panoramic view across the upper hill area from the Cordillera Cahuapanas............... 81
Fig. 3.62: View of a typical slope in the upper hill area with vegetation of decreasing growth density and height . 82
Fig. 3.63: View into a valley with the palm Jessenia bataua along the ravines and on the bottom. ...................... 82
Fig. 3.64: Close view of an upper slope with light green colored shapumbal ground cover. ................................ 82
Fig. 3.65: View of a shapumbal slope, easily recognizable with many bright and shiny stems visible. ................. 82
Fig. 3.66: Aspect of a wet chamizal with many small and short stemmed individuals and Mauritiella peruana. .... 82
Fig. 3.67: Aspect of a dry chamizal with considerably higher vegetation. ........................................................... 82
Fig. 4.1: Overview of relationships between statistical classification units generated by the soil, biometric and
combined approach. .......................................................................................................................... 85
Fig. 4.2: Proposed key for ground based classification of vegetation units in the study area............................. 102
Fig. 9.1: Schematic sketch illustrating the approach for geometrical correction of field measurements.............. 118
Fig. 9.2: Dendrogram generated by Hierarchical Cluster Analysis using Single-Linkage-Method ...................... 121
Fig. 9.4: Dendrogram generated by Hierarchical Cluster Analysis using Single-Linkage-Method. ..................... 125
Fig. 9.6: General overview of the study area on a panchromatic aerial photograph and plot locations
over different geological formations. ................................................................................................. 132
Fig. 9.7: Digital Elevation Map for the core section of the study area. .............................................................. 133
Fig. 9.8: Map of slopes in the core section of the study area as derived from the Digital Elevation Map. ........... 133
V
Contents
Table of Tables
Table of Tables
Tab. 2.1: Record of major seismic events in the Alto Mayo Region over the past 60 years..................................11
Tab. 2.2: Plot sizes for individual approaches biometry, biomass harvest, structure and soil assessment............19
Tab. 2.3: General site specific parameters. .......................................................................................................20
Tab. 2.4: Soil parameters from field and laboratory analyses. ............................................................................21
Tab. 2.5: List of structural parameters recorded in the field................................................................................22
Tab. 2.6: Biometric parameters recorded in the field..........................................................................................23
Tab. 2.7: Mean densities derived from actual density measurements for soil horizon classes. ............................24
Tab. 2.8: List of poorly and highly represented structural parameters from the field study. ..................................25
Tab. 2.9: Biomass distribution classes calculated for tree height and gbh for statistical characterization..............26
Tab. 2.10: Rating of “Measure of Sampling Adequacy” for the suitability of a parameter for PCA. .......................30
Tab. 3.1: Soil parameters and their “Measure of Sampling Adequacy” expressing their suitability for PCA...........34
Tab. 3.2: Eigenvalues of the three factors extracted by Principal Component Analysis. ......................................35
Tab. 3.3: Mean slope, elevation, and exposition as used to evaluate the topography of the soil units. .................42
Tab. 3.4: Comparison of means for all classified soil units for thickness of topsoil, pH, N, P, and K. ....................47
Tab. 3.5: Spearman’s correlation coefficient matrix generated for all biometric classes.......................................48
Tab. 3.6: t-values calculated for all biomass parameters found in the individual biometric clusters. .....................50
Tab. 3.7: Significant Spearman’s correlation coefficients tween soil parameters and structural parameters. ........57
Tab. 3.8: Comparison of all classified biometric units for tree height, dbh, basal area, stem density, & biomass. .58
Tab. 3.9: Assessed parameters and their “Measure of Sampling Adequacy” expressing their suitability for PCA. 59
Tab. 3.10: Eigenvalues of the four factors extracted by Principal Component Analysis. ......................................60
Tab. 3.11: Terrain inclination, elevation & slope exposition to characterize the topography of the classified units.62
Tab. 3.12: Comparison of all classified units by the combined approach for thickness of topsoil, pH, N, P and K. 66
Tab. 3.13: Comparison of all classified units by the combined approach for tree height, dbh, basal area, stem
density, biomass and stand characters. ...........................................................................................69
Tab. 3.14: Slope distribution in the study area...................................................................................................80
Tab. 4.1: Biomass, stand height, and basal area of tropical rainforests. .............................................................94
Tab. 4.2: Occurrence and classification of tropical heath forests. .......................................................................97
Tab. 4.3: Floristic elements linking the chamizales of the Alto Mayo with other tropical heath forests. .................99
Tab. 9.1: Allometric equations after OGAWA et al. (1965) used for biomass calculation......................................117
Tab. 9.2: Regression values for factor scores on each plot. .............................................................................120
Tab. 9.3: List of factor loadings for the soil parameters used for the categories general, nutrients, and cations..120
Tab. 9.4: t-values calculated for all soil parameters within the soil cluster (1): “Level Acidic Sand Soils”. ...........122
Tab. 9.5: t-values calculated for all soil parameters within the soil cluster (2): “Steep Litter Soils”......................122
Tab. 9.6: t-values calculated for all soil parameters within the soil cluster (3): “Aluminum / Clay Soils”. .............122
Tab. 9.7: t-values calculated for all soil parameters within the soil cluster (4): “Carbon Sub-Soils”.....................123
Tab. 9.8: t-values calculated for all soil parameters within the soil cluster (5): “Rich Base Cation Soils”.............123
Tab. 9.9: Key to particle size distribution in texture as classified in the field. .....................................................123
Tab. 9.10: Regression values for factor scores on each plot. ...........................................................................124
Tab. 9.11: List of factor loadings for the soil parameters used for the categories soil, biometry, and structure....124
Tab. 9.12: t-values calculated for all classified units within the cluster (1):“Dry Chamizal” (Drained Plateaus). ...126
Tab. 9.13: t-values calculated for all classified units within the cluster (2):“Wet Chamizal” (Peaty Plateaus). .....126
Tab. 9.14: t-values calculated for all classified units within the cluster (3): “Dry Rainforest” (Upper Slopes). ......126
Tab. 9.15: t-values calculated for all classified units within the cluster (4): “Shapumbal” (Steep Disturbed Upper
Slopes). ......................................................................................................................................126
Tab. 9.16: t-values calculated for all classified units within the cluster (5): “Impoverished Rainforest” (Ridge)....127
Table of Tables
Contents
VI
Tab. 9.17: t-values calculated for all classified units within the cluster (6): “Palm Rich Rainforest” (Ravine). ..... 127
Tab. 9.18: t-values calculated for all classified units within the cluster (7): “Premontane Rainforest” (Valley)..... 128
Tab. 9.19: t-values calculated for all classified units within the cluster (8): “Low Canopy Rainforest” (Lower
Slopes). ....................................................................................................................................... 128
Tab. 9.20: Significant distinction of soil clusters by parameters from soil, biometry and structure parameters. .. 129
Tab. 9.21: Significant distinction of biometry clusters by parameters from soil, biometry, structure parameters. 129
Tab. 9.22: Significant distinction of clusters from combined analysis by parameters from soil, biometry and
structure ...................................................................................................................................... 129
Tab. 9.23: Comparison of all recorded plots for their biometric properties: tree height, dbh, basal area, stem
density, biomass and stand characters. ........................................................................................ 130
Tab. 9.24: Statistical Overview for Cluster Parameters used in the Combined Analysis.................................... 131
Tab. 9.25: Plot Locations. .............................................................................................................................. 134
Tab. 9.26: Plot Assignments to Soil, Biomass, and Vegetation Units. .............................................................. 135
Tab. 9.27: Average values of Soil per Plot. Particle Size Distribution , Thickness, C/N ratio, and Carbontot content
.................................................................................................................................................... 160
Tab. 9.28: Average values of Soil per Plot. Macronutrients Nitrogentot, PhosphorusOlsen , and Potassium.......... 161
Tab. 9.29: Average values of Soil per Plot. Macronutrients Calcium and Magnesium, and exchangeable Iron. . 162
Tab. 9.30: Average values of Soil per Plot. Non-Base Cations Aluminum, exchangeable Protons, pH, CECeff,
and BS. ....................................................................................................................................... 163
Tab. 9.31: Physical and Chemical Soil Properties per Horizon. Plots 01 - 12 ................................................... 164
Tab. 9.35: Structure Parameters used for Statistical Analysis. Dimensions and Sizes. ..................................... 168
Tab. 9.36: Structure Parameters used for Statistical Analysis. Shapes. ........................................................... 169
Introduction
1
1 Introduction
This study attempts to classify the endangered primary rain forests at the foothills of the
Cordillera Cahuapanas in the Alto Mayo region of Peru. The aim was to statistically distinguish
and describe the different vegetation units in the study area based on data from soil investigation,
biometry measurements and vegetation structure determination. The study quantifies stocks of
nutrients and biomass, identifies environmental hazards and evaluates the importance of these
threatened natural resources.
The situation of the primary forests in the Alto Mayo Valley, in which the study area is
located, is typical for the forests of the eastern Andes of Peru. In 1975 the Carretera Marginal, a
road linking the Amazon lowlands with the central Andes and the Pacific coast, was completed. It
initiated a continuous influx of rural settlers from the densely populated Andes. Local
preconditions of soils and climate and the sustainable agricultural practices for this region were
unfamiliar to the majority of these settlers. By 1998, approximately 140 000 ha of primary forest
had been cleared for agricultural use in the Alto Mayo Valley. Almost one-seventh of this area has
already been abandoned due to low crop yields (ELLIOT 1998). Along the main roads leading
through the flat valley bottom along the Alto Mayo River, forests are now either destroyed or
strongly degraded. Primary forests are limited to inaccessible areas above 1 500 m a.s.l or to
extremely rugged terrain. Relatively untouched by clear-cutting or agriculture are territories that
were recently assigned to indigenous Aguaruna tribes in the area.
Tropical mountains feature a high variability of climates, soils, and relief that host a unique
biodiversity in numerous forest types (GENTRY & ORTIZ 1993). YOUNG (1992) proposes an
altitudinal zonation of pristine forests covering the eastern slopes of the Peruvian Andes. He
describes tropical lowland forests from 200 m a.s.l. to 500 m a.s.l.. Premontane rain forests
dominate in 500 m a.s.l. - 1 500 m a.s.l., the montane zone includes a lower montane rain forest
from
1 500 m a.s.l. - 2 500 m a.s.l.
and
an
upper
montane
rain
forest
from
2 500 m a.s.l. - 3 500 m a.s.l. These forest types vary with respect to their tree species
composition, their stand structure and their ecological functions (RICHARDS 1996, GIVNISH 1999).
Floristic aspects of tropical forests have been studied for quite some time (W HITMORE 1993).
Numerous studies exist on the structural characteristics, since the high diversity of forest species
makes taxonomically-based studies difficult (PAULSCH & CZIMCZIK 2001). On a global scale,
tropical mountain forests belong to the genetically most diverse ecosystems (GENTRY 1992).
Plant structure can be interpreted as an indicator of ecophysiological adaptations closely linked to
existing site quality and abiotic conditions (VARESCHI 1980, W ERGER et al. 1982, RICHARDS 1996).
The complex ecosystem functions of tropical montane forests have been increasingly studied.
Focus was often laid on soil preservation and prevention of erosion in steep terrain, the buffering
effect for local water cycles, as well as carbon storage in biomass (BRACK & MENDIOLA 2000,
BMELF 1999).
2
Introduction
Degradation and destruction of tropical forests progresses at a fast pace. However, our
understanding of the complex processes and the interactions constituting these ecosystems
develops slowly. Primary tropical forests are mainly converted into agricultural land by slashing
and burning. Large scale infrastructure and development programs, often in connection with
poorly administered timber extraction, contribute to the decrease in forest areas (ALBRECHSTRUCKMEYER 1991, BMELF 1999). Studies by the FAO (1999) show that the loss of tropical
forest area from 1990 - 1995 was in the order of 12.5 Mio ha per year. In Peru alone, the annual
rate of forest clearing was estimated to be 217 000 ha.
The destruction of the forests has far-reaching ecological and economic consequences.
Sustainable agricultural cultivation in the moist tropics is limited to favorable sites, which do not
exist in the major part of tropical moist forest regions (BMELF 1999). The soils of the Alto Mayo
region have a low potential for long-term agricultural use (ONERN 1982). Continuous degradation
of these soils due to inappropriate agricultural cultivation techniques has led to the
impoverishment of the rural population and has already reached the subsistence level in many
cases. Amelioration of degraded soils by liming or fertilization or increasing crop yields by the use
of herbicides is expensive and thus not feasible for the farmers. Instead, degraded soils are
commonly abandoned (DEFORPAM 1997, KAUFFMAN et al. 1998, BMELF 1999) which results in
mounting pressure on the remaining natural land resources. Abandoned areas are quickly
covered by ferns (Pteridium aquilinum) forming extended unproductive badlands, locally referred
to as shapumbales. This vegetation inhibits secondary forest growth for many years. The
extension of man-made shapumbales in the Alto Mayo Valley in January of 1997 was estimated
to be 25 000 ha (DEFORPAM 1997), in March of 1999 more than 30 000 ha (DEFORPAM 1999).
Both the local population and the government land management agencies in Peru generally
value forests only for exploitation and agricultural development. Sustenance considerations such
as environmental protection and preservation of natural resources are of minor importance
compared to the short-term economic gains (BMELF, 1999). In order to advance a sustainable
agricultural and forestry development in the region, the German Society for Technical
Cooperation (Deutsche Gesellschaft für Technische Zusammenarbeit, GTZ) has supported and
consulted the local Peruvian development and management agencies of the Proyecto Especial
Alto Mayo (PEAM) in Moyobamba through their project Desarrollo Integral Alto Mayo (DIAM)
since 1997. Special attention has been on the watersheds of the Río Avisado and Río Tioyacu,
where contiguous tracts of primary valley rain forests can still be found.
The Forest Ecology and Remote Sensing Group (FEARS) at the Ecological Botanical
Gardens (ÖBG) of the University of Bayreuth has been studying the ecology of this area in
cooperation with the Universidad Nacional de San Martín, Facultad de Ecología in Moyobamba.
Until 2003, it is intended to provide data to support management decisions for land use planning
and the identification of areas which require special preservation in a natural state. The study
Introduction
3
area is characterized by less favorable site conditions such as flooding, poor drainage,
susceptibility to slope erosion, or poor soil nutrient status. All studies are designed to serve as a
planning tool for the identification of protected forest areas.
The work presented in this study is part of a detailed inventory of the existing forest types and
their site conditions and aims to provide a base for the characterization of the watersheds. The
classification scheme described within for soil and forest properties combines the analysis of
stand structure and quantitative parameters of forest stands with their chemical and physical soil
properties.
The forests
of
the upper watersheds
of
the Río Tioyacu were classified by
DEMPEWOLF (2000), while BÖRNER (2000) investigated the inundated forests in the lower
watersheds and the lower hill region. The present study focuses on the upper hill area. This
region stretches to the northeast from the last outposts of current settlements towards the
southern piedmonts of the Cordillera Cahuapanas. The area supports a significantly different
vegetation cover of the upper hill area compared to the lower hill region. The forest stands in this
study are all located between approx. 900 and 1 100 m a.s.l. and are premontane forests
according to YOUNG (1992). Located at an elevation of approx. 1 000 m a.s.l. near the flanks of
the Cordillera Cahuapanas the study region features several small plateaus covered by a stunted
and open vegetation formation mainly on poorly drained soils. This vegetation is structurally and
floristically similar to the heath forests described for the upper slopes of the Cerro Tambo
(DEMPEWOLF 2000, METTE 2001), the chamizal vegetation described for the Peruvian Amazon
lowlands by RUOKOLAINEN & TUOMISTO (1993), and the white sand savannah for the tropical
northeast of South America by COOPER (1979) and by SPECHT (1979) for other tropical regions.
The extremely dissected upper hill region displays a highly variable vegetation cover on a
small scale. The pattern of vegetation type distribution may be interpreted either as a random
patchwork over the entire upper hill area or bound to site specific factors, e. g. topographic
position. It seems likely that a highly variable relief creates differences both in microclimatic
conditions as well as soil conditions resulting in different vegetation types on a small scale. If
abiotic factors govern the current vegetation distribution patterns, the analysis of topographic and
edaphic conditions should result in a good correlation between topography, pedology and
vegetation type.
4
Introduction
In order to test these assumptions the following four null-hypotheses were formulated to be
tested in the study area:
I. Soil chemistry and soil texture do not vary in the upper Río Avisado watershed.
Rejection of this hypothesis would justify an investigation if different edaphic
conditions followed topography and if soil status had an effect on the development of
the vegetation types.
II. Above-ground stand biomass is homogeneously distributed within the upper Río
Avisado watershed.
Rejection of this hypothesis would suggest that based on the biomass of the forest
stands there are differences among the vegetation which can be assessed and
quantified biometrically.
III. There are no significant differences in vegetation patterns between different
topographic situations.
Rejection of this hypothesis would confirm that site topography may influence the
development of the vegetation types.
IV. Separation of the Río Avisado forest types requires comprehensive information on
both: edaphic site conditions and stand structure.
Rejection of this hypothesis would indicate that there exists a certain proportion of
redundancy in the dataset and that omitting these redundant parameters would lead
to a streamlined and solid classification scheme for the study area.
The present study intends to provide a baseline for the spatial distribution of different forest
types in the upper hill region of the Río Avisado watershed.
6
Materials and Methods
Study Area
2 Materials and Methods
2.1 Study Area
The following chapter will outline the natural history and current conditions encountered at the
study site.
2.1.1 Location and History
The study site is located on the eastern slopes of the Andes of North Central Peru at UTM
18S 261056 / 9370052 in the upper Río Mayo Valley, approximately 35 km north of the city of
Moyobamba.
Peru covers a territory of 1.28 Mio km2 on the Pacific coast of tropical South America
between 0° and 18° S, and from 69° to 81° W. Young (1992) divides the country into three natural
regions from west to east: a) the narrow, arid coastal plain of 30 to 60 km width locally termed the
“Costa”, b) the Andean highlands (highest peak Mount Huarascarán 6 768 m a.s.l.) called the
cordillera or “Sierra”, and c) the region east of the main Andes Cordillera which is potentially
covered by tropical rainforest. The latter can be subdivided into the “Selva alta” on the
Subandean Belt and the “Selva baja” in the Amazon Basin (Fig. 2.1).
Fig. 2.1: Location of the study site
indicated on an edited
NOAA-AVHRR satellite
image of Peru, covering
-67° to -83° W and -1° to
-18° S.
Source: AXION 1997
Color coded are the
geological units according to
YOUNG (1992) from left to
right:
Coastal
Plain
(yellow),
Cordillera
(brown),
Subandean Belt (dark green)
and Amazonian Plain (green).
Coinciding is the local
zonation into Costa, Sierra
and Selva alta and baja.
Study Area
7
The reason for such a spatially tight sequence of different biomes are the high Andean
cordilleras which act as a climatic barrier. The contiguous Peruvian Western Cordillera
(“Cordillera Occidental”) restricts the drought effects of the cold Humboldt current and the impact
of irregular decadal events of El Niño to the coastal plains. In addition, the Western Cordillera
shields this region from moist air masses from the Amazon basin further inland. The slopes of the
Eastern Cordillera (“Cordillera Oriental”) of Peru are exposed to the humid climate of the Amazon
rainforest and are incised by tributaries to the Amazon river.
The term “Alto Mayo” refers to the upper catchment of the Mayo river that belongs to the Río
Huallaga-Río Amazonas drainage system. The valley trends NW-SE along the eastern slopes of
the Andes on the brink between Selva alta and Selva baja yet still within the Subandean Belt. The
upper Río Mayo is a white water river carrying high sediment loads and is embedded into a broad
valley of approx. 50 km width.
The Alto Mayo region covers approx. 40 000 km 2, belongs to the department of San Martín. It
is divided between the provinces of Rioja and Moyobamba. Total population of the Alto Mayo is
estimated to be almost 200 000 (INADE-PEAM 1999).
The study area is framed by the Cordillera Cahuapanas mountain ridge in the north. This
ridge is part of the Cordillera Occidental and which separates the Alto Mayo Valley from the
Amazon lowlands (cf. Fig. 2.1). The Cerro Tambo massif in the west belongs to the Cordillera
Cahuapanas. The study area extends from 800 m a.s.l. in the valley bottoms to approx.
1 840 m a.s.l. in the Cordillera Cahuapanas. It encompasses the watersheds of two left bank
tributaries to the Río Mayo, the nutrient poor black water rivers Río Tioyacu and Río Avisado. The
Río Tioyacu emerges at 1 050 m a.s.l. north of the Cerro Tambo massif and discharges into the
Rio Mayo south of Rafael Belaunde. Its watershed covers approx. 100 km 2 (BÖRNER 2000) and is
limited to the west by the Río Cachiyacu watershed. The headwaters of the Río Avisado are at
1 200 m a.s.l. on the SE slope of the Cordillera Cahuapanas and flow in southerly direction east
of the Río Tioyacu. North of the settlement of Valle de La Conquista, the Río Avisado splits into a
system of several meandering branches that reunite near the settlement of Tingana. The Río
Avisado finally drains into the Río Mayo in two branches west of Buenos Aires. Its watershed
covers approx. 360 km 2 (BÖRNER 2000. It is limited by the Río Huascayacu watershed to the
east. This study was conducted on the southern slopes of the Cordillera Cahuapanas which
include the highly dissected upper hill area (approx 850 - 1 050 m a.s.l.) and the piedmonts of this
mountain range between approx. 1 000 and 1 200 m a.s.l. (cf. Fig. 2.11 in chapter 2.1.6).
Until the early 1970’s indigenous Aguaruna tribes were the only people to make use of the
land by hunting and an approximate 10 year cycle of shifting cultivation. The construction of the
main road (“Carretera Marginal”) in the early 1970’s connected the northern Selva with the coast
in 1975 and triggered a tremendous influx of settlers from the overpopulated highlands that
continues today. In search for arable land the easily accessible pristine forests of the valley
bottom were burnt and logged without control in order to give way for rice terraces (cf. Fig. 2.1 in
8
Study Area
chapter 2.1.6). Currently, the land conversion extends into the comparatively less colonized small
valleys of the tributaries of the Río Mayo but has slowed down in the last years since the
acquisition of new land requires local government permission. In order to prevent further intrusion
and illegal colonization by settlers, a large part of the remaining pristine forests was adjudicated
in 1999 to the Aguaruna tribes. These tribes now live in a few permanent settlements
(Comunidades Nativas) and apply modern agriculture. The field work for this study was carried
out on the territories of the Comunidades Nativas Cachiyacu, Kusu and Shimpiyacu.
Concurrent with the colonization comes slashing and burning of primary forest to create
agricultural areas (cf. Fig. 2.12 in chapter 2.1.6). These areas are often abandoned after a few
years due to their diminishing fertility (cf. Fig. 2.9 in chapter 2.1.6). Plantations by the settlers are
mostly family-run farms of a few hectares. A small area is always reserved for self sustenance
and local trade, e. g. manioc, bananas, potatoes and pineapple. Main cash crops for export are
wet and dry rice produced in the valleys and coffee on slopes above 900 m a.s.l.. An estimated
90 % of the rice production covers the Peruvian demand and 10 % is exported. Coffee (mainly
Coffea arabica) is almost entirely an export product. The coffee plantations replaced earlier
plantations of coca (Erythroxylum coca), which were given up after governmental enforcement
increased in the mid 1990’s. Abandoned coca fields are often covered by ferns (Pteridium
aquilinum, so called shapumbal-vegetation) which are a typical component of the region. When
the shapumbales are not constantly burnt (a common practice without obvious reason, cf.
Fig. 2.10 in chapter 2.1.6) secondary forest develops and is recognizable by fast growing
Cecropia sp. trees.
Due to reasons of accessibility the bulk of investigation plots were recorded along a ridge in
the upper hill area of the eastern Río Avisado watershed. In order to also cover Cretaceous
substrate, three plots were examined at comparable altitude near the upper Río Tioyacu (cf. also
Fig. 2.1).
Study Area
RIOJA (880 m)
9
22.9 °C 1525 mm
[20 - 27]
06°02' S 77°09' W
n.d.
29.2
Fig. 2.2: Climate diagram of
Rioja, Alto Mayo,
North Central Peru.
Rioja is located approx.
30 km SW of the study
area. Data was collected
1964 - 83 for temperature
and
1964 - 93
for
precipitation.
The diagram is assembled
according to the scheme
outlined by WALTER et al.
(1975).
~10.0
10.3
n.d.
J
A
S
O
N
D
J
F
M
A
M
J
Source: OBANDO 1995
2.1.2 Climate
The climatic conditions along the eastern slopes of the Andes vary primarily with orography.
Moist air drifting westward from the Amazon basin is blocked by the eastern cordillera where
large cloud masses form and rise along the mountain slopes in approx. 1 200 - 2 000 m a.s.l.. The
annual mean temperature drops from 25 °C (< 1 000 m a.s.l.) to 9 °C (> 3 500 m a.s.l.), and the
annual sum of precipitation drops from 7 000 to 500 mm (YOUNG, 1992). The region is influenced
by tropical trade winds that drive moist air masses across the Amazon lowland in westerly
direction (KNOCH 1930) and cause two precipitation maxima in November and in March when the
inter-tropical low pressure belt (ITC) is centered to the South of the equator (NOBRE et al. 1991).
The only pronounced dry season is observed between the months of June and August.
Few data sets exist for precipitation and temperature in this region, and data for sites above
900 m a.s.l. are missing entirely. Continuous measurements for about 20 years are available for a
climate station in Rioja (OBANDO 1995) which is located on the right bank of the flat valley bottom
of the Río Mayo at 880 m a.s.l. (UTM 18 S: 262020 / 9332641) about 30 km south west of the
study area (Fig. 2.2). According to KÖPPEN (1936) the climate of the Alto Mayo Valley is classified
as tropical perhumid jungle climate (Afh) since the mean temperature of the coolest month
(22.3 °C in July) and the mean annual temperature (22.9° C) in Rioja exceed 18° C and the
average minimum precipitation each month is more than 60 mm (average minimum in July
62.6 mm). However, occasional dry spells can be observed when several consecutive months
receive less than 50 mm of rainfall. This was the case in 1961 and 1979 (Fig. 2.3). The mean
annual precipitation is 1 525 mm. The annual sum of precipitation for areas above 1 000 m a.s.l.
is estimated to be much higher between 3 000 and 4 100 mm (ONERN 1982). The reason for the
higher precipitation on elevations between 1 200 m a.s.l. and 2 000 m a.s.l. are the eastern Andes
Monthly Precipitation [mm]
10
Study Area
400
Moyobamba
Rioja
300
200
100
0
1960
1962
1964
1966
1968
1970
1972
1974
1976
1978
1980
1982
1984
Fig. 2.3: Monthly sum of precipitation
from 1959 to 1984 for two
meteorological
stations
at
Moyobamba and Rioja in the
Alto Mayo Valley, Northern
Peru.
Source: OBANDO 1995
which block easterly winds carrying moist air from the Amazon basin. Meteorological
investigations in the studied watersheds by METTE (2000) showed that precipitation increases
towards the slopes of the bordering mountain ranges and reaches 2 200 mm per year at
1 400 m a.s.l. (Cerro Tambo, UTM 18 S: 249017 / 9367930). The dry season is less pronounced
and annual mean temperature drops to 19.4 °C. This trend can be assumed to continue in the
entire study area towards the slopes of the Cordillera Cahuapanas. Therefore, values recorded at
the weather station in Rioja should be considered minimum values for precipitation and maximum
values for temperature with respect to the study region.
Interannual variation of precipitation may be tele-connected to large scale global oscillations,
like the El Niño, (El Niño Southern Oscillation = ENSO) phenomenon. However, it is conceivable
that the massive Andean cordilleras act as a shield from coastal climatic events. Therefore, it is
not surprising that climatic records from the past 40 years do not display any significant
correlations between the annual variability of precipitation in the Alto Mayo Valley and the South
Pacific current sea surface temperature deviation (ZIMMERMANN et al. 2002), a parameter
5
4
Deviation [K]
Sea Surface Temperature
commonly used to quantify ENSO activity (MESTAS-NUÑEZ & ENFIELD 2001).
South Pacific Tropical Current
3
2
SD
1
0
-1
mean
r2=0.025
-2
Annual Precipitation [mm]
1960
1970
1980
1990
2000
2500
Alto Mayo Valley, Peru
2000
SD
1500
mean
SD
1000
r2=0.025
1960
1970
1980
1990
2000
Source: MESTAS-NUÑEZ & ENFIELD 2001, OBANDO 1995
Fig. 2.4: Comparison of El Niño events
to the precipitation situation in
the Alto Mayo Valley.
Above, the graph describes the
mean sea surface temperature
deviation (SST) for the South Pacific
tropical current from 1958 until
1998.
Below, the annual precipitation in
the Alto Mayo Valley is plotted,
measured in Moyobamba. (Data
from 1984 - 1986 are missing due to
terrorist problems).
Study Area
11
Tab. 2.1: Record of major seismic events that affected the Alto Mayo Region over
the past 60 years.
Date
Magnitude
Epicenter
Reported Damage
06 Aug 1945
n.d.
South of Moyobamba
Cracks in ground
10 Nov 1946
n.d.
Sihuas
Material damage
15 Jun 1954
n.d.
n.d.
Material damage
15 casualties
19 Jun 1968
6.9
Moyobamba
29 May 1990
6.4
Rioja
70 casualties
04 Apr 1991
6.2
Angaiza (Cahuapanas)
40 casualties
Source: CUADRA LIÑAN & CHONG CHONG 1991
2.1.3 Geology and Geomorphology
The uplifting of the Andes occurred at the western edge of the South American plate due to
plate tectonic movements during the middle Cretaceous period (approx. 100 Mio years BP). The
last major uplift phase took place during the Pliocene period when the Andes attained close to
their current height. On a small scale uplift continues to the present day and is manifested by
occasionally recurring seismic activity that also affects the Alto Mayo region (Tab. 2.1).
The Alto Mayo Valley belongs to the Subandean mountain belt which separates the high
Andes from the Amazon basin according to YOUNG (1992) (cf. Fig. 2.1). During the Triassic and
Cretaceous periods the region was covered from the east by transgressing oceans. This led to
the deposition of marine sediments which were folded during the second uplift phase in the mid
Miocene until the early Pliocene (Tectonic map of South America 1 : 5 000 000, 1978). The oldest
sediment layer is most likely the Triassic-Jurassic limestone formation Pucará NW of Rioja
(ONERN 1982). All slopes to the west of the Río Mayo consist of limestone formations. High
sediment loads make the Río Mayo a white water river. To the east of the Río Mayo the valley is
limited by the Cordillera Cahuapanas, a range consisting primarily of sandstones. The valley
bottom and areas up to approx. 800 m a.s.l. are covered by relatively young sedimentary deposits
of unknown depth.
The watersheds of the Río Tioyacu and Río Avisado are dominated by Jurassic, Cretaceous,
Tertiary and Quaternary sediments (Fig. 2.5). The study area is comprised of predominantly
Tertiary and Quaternary sediments although both rivers originate from the piedmonts of the
Cordillera Cahuapanas over Jurassic and Cretaceous rocks. The wide river floodplains of the Río
Mayo feature primarily Holocene, fluvial, loamy and clayey sediments (Qh-fl) that have been
deposited during flooding events. Slightly older alluvial deposits of varying clay and loam layers
can be found along the seasonally flooded higher river terraces (Qh-al). Near the southern slopes
of the Cordillera Cahuapanas this formation includes patches of deposited white sands containing
a thin layer of volcanic material (ONERN 1982) that form extended plateaus. The lower hill region
consists of sediments from the Yahuarango formation (Tp-y). These are considered to originate
12
Study Area
Fig. 2.5: Geological map of the
upper Río Tioyacu and
Río Avisado watersheds.
Sources: INGEMMET 1996, IGN 1996
from the Paleocene to early Eocene periods (INGEMMET 1997) under predominantly fluvial to
lacustrine conditions. Lithologically, the formation consists of sandstones with intermittent clay
layers and local occurrences of conglomerates. To the north of the settlements of Paz y
Esperanza and Gamínedes the lower hill region transforms into the upper hill region which rises
to elevations of 1 000 m a.s.l.. The hills consist of alluvial Pleistocene sediments (Qp-al)
comprising thin horizontal layers that vary from sands to clays. These layers represent the
different uplift phases of the Andean Cordillera during the Pleistocene period.
The main block of the Cordillera Cahuapanas and especially the Cerro Tambo massif at the
headwaters of the Río Tioyacu are made up of early Cretaceous rocks of the Cushatabay
formation (Ki-c). This formation features whitish-gray sandstones of medium to coarse texture
which occasionally include micro-conglomerates under inclined, layered stratigraphy with channel
structures and varying proportions of loamy sand. This structure led to the conclusion that
deposition occurred in a high energy environment as a fluvial delta sediment. Transition to the
late Jurassic Sarayaquillo formation on the lower slopes of the Cordillera Cahuapanas may be
gradual. Rocks of the Sarayaquillo formation are described as reddish-brown quartzitic
sandstones of fine to medium texture that are firmly packed and crumbly. Sediment structure
indicates that sedimentation took place under semiarid conditions into the floodplains of an intramontane basin (INGEMMET 1997).
Study Area
13
N
5
32
2
3
3
1
1
1
4
Fig. 2.6: Radar image of the
upper Río Tioyacu and
Río Avisado watersheds.
Look direction is from the
east (right hand side).
6
Source: RADARSAT 1999
Settlements are marked red
and the rivers Tioyacu and
Avisado are outlined in blue.
Clearly visible is the dissected
and eroded table land of the
upper hill area .
Non-eroded small mesas on
the foot of the cordillera .
Also the younger Quaternary
sediments in the river valleys
are distinguishable .
Landmarks are the Mayo
River ,
the Cerro Tambo ,
and the settlement of Gamínedes .
1
4
6
2
3
5
The Tertiary and Pleistocene sediments of the upper and lower hill areas were most likely
modified by strong erosion processes during or after the Pleistocene period which created the
currently highly dissected and hilly relief (Fig. 2.6) with narrow crests and elevational gradients
from 30 to 100 m (cf. digital elevation model in Fig. 3.58). Only a lack of the protective vegetation
cover can have permitted such extreme erosion within a short period of time. Since LAUER (1986),
VAN DER
HAMMEN (1974) and W HITMORE (1991) found that the Pleistocene timberline depression
during the last glacial maximum (LGM) did not reach as far down as the floors of the Alto Mayo
Valley another explanation for the erosion forms should be sought. Evidence of large scale fires
have repeatedly been reported by several authors for the Amazon lowlands during the past 2 000
years (SANFORD et al. 1985, MEGGERS 1994). They link these fires to drought effects by extreme
El Niño events. However, impacts of such ENSO dynamics were found to be marginal on the
study region today. Local scientists report that the entire upper valley of the Río Mayo may have
been flooded by a large lake which drained rapidly in up to three stages possibly after decisive
seismic events leaving the former lake floor to unimpeded erosion (PERISUTTI, 2002, pers.
communication). Under these conditions the stability of the geologic substrate may have
contributed to the relatively sharp division of the different landforms such as the lower and upper
hill regions and the mesas further towards the imaginary shoreline. This would also provide an
explanation for the conspicuously even level of the ridgelines which imply an initially plain surface
on the bottom of such a sedimentary trough (cf. Fig. 2.11 in chapter 2.1.6).
Approximately 90 % of the watersheds of the Río Tioyacu and Río Avisado are currently
covered by dense forests. The stand canopy heights range from 20 to 35 m and effectively
14
Study Area
prevent any large scale erosion (cf. aerial photograph in Appendix 9.3.1). Lateral erosion occurs
along the creeks during high precipitation events. Also local failures of steep slopes (> 25°) may
be fostered by heavy rainfall and by earthquakes (THOMAS 1994).
2.1.4 Soils
Soils have been studied with respect to agricultural projects (ONERN 1982, ONERN/PEAM
1989) in some accessible areas of the lower Río Avisado watershed and were classified
according to Soil Taxonomy (SOIL SURVEY STAFF 1998).
A clear pedological separation of two units was found for the area. The alluvial and fluvial
sediments of river terraces are covered by soils which are influenced by periodic flooding,
stagnant water and / or high groundwater levels. Most of them developed to Tropofluvents or
Tropaquepts. Tropofluvents are tropical bottomland raw soils of river valley bottoms whereas
Tropaquepts occur on alluvial sites with a loamy-clayey texture showing indications of heavy
gleys due to high groundwater tables. The Corg-content is generally high, and layers of
undecomposed organic material may be interspersed. The nutrient content is high due to external
input by flooding.
In contrast, soil development in the hill region is dominated by topographic position. The
slopes of the lower hill region support mainly Dystropepts and dystric Tropudults. Dystropepts are
only slightly developed acidic soils with a low base saturation and high Al3+ concentrations.
Dystric Tropudults are also poor soils that contain a shallow horizon with clay accumulation and a
depth of 50 - 75 cm. According to the above studies Troporthents prevail in the upper hill region.
These are tropical, generally acidic raw soils on slopes which are easily eroded. BÖRNER (2000)
found on a catena of soils in the lower hill region a highly variable texture ranging from clay to
loam which reflects the small-scale variability of sedimentary layers of the parent material. On
steep slopes the horizon depth and sequence may change within a few meters of elevation. Soils
on the slopes are generally well drained. The increased susceptibility for soil erosion, the poor
nutrient supply, and an Al3+ saturation of generally 40 - 80 % limit these soils for agricultural use.
Study Area
15
Source: INRENA 1995
Fig. 2.7: Location of the study
site highlighted on a
section of the forest type
map of Peru 1 : 1 000 000.
Df
Deforested
Bh mo
Montane Rainforest
Bh cb
Lower Hill Rainforest
Pj
Grassland
Bh ca
Upper Hill Rainforest
Bh tm
Alluvial Plain Rainforest
Indicated are the four
major topographic units: The
Andes, the broad Alto Mayo
Valley, the Cordillera Cahuapanas and the adjacent
Amazon plain. Only the south
eastern parts of the study area
are classified as deforested
areas whereas the remaining
part is mainly covered by
montane rainforest and upper
hill rain forest according to
this map. This study focuses on
the upper hill rainforest.
2.1.5 Vegetation
The study area belongs to the Neotropis (LAUER 1986). According to the HOLDRIDGE system
of life zones, the study area belongs to the premontane moist forest bioclimatic zone with an
average temperature between 18 and 24 °C, and average annual precipitation from 1 000 to
2 000 mm that drops below 100 mm for 2 - 4 months (READING et al. 1995).
The biogeographical atlas published by SCHMITHUESEN (1976) shows the Alto Mayo region as
tropical montane rain forest. The vegetation map of HUECK & SEIBERT (1981) classifies the forests
of the study area as evergreen mountain rain forest, lower level, on the east facing slopes of the
medium Andes (Selva de Yungas). They are described as rain forests of 30 m in height, having
abundant lianas and epiphytes, many palm species (Iriartea, Astrocaryum, Phytelephas,
Carludovica), and distinct Cecropias and Ochroma lagopus.
According to PRANCE (1989) the study area is covered by lower montane forest 700 - 1 200 m
that is considered to be quite similar to lowland rain forest. The species composition gradually
changes towards upland forests featuring fewer woody vines, abundant vascular epiphytes, and
predominantly mesophyll leaf types.
GRUBB (1974) drew the limit between lower montane forests and lowland rain forest in the
Andes between 700 and 1 200 m a.s.l.. The lower montane forests tend to be shorter than their
counterparts of adjacent lowland areas, again with fewer woody vines, more vascular epiphytes
and predominantly mesophyll leaf types. The upper limit of the lower montane rain forest was
observed by GRUBB (1974) between 1 800 and 2 400 m a.s.l. where it transforms into the upper
montane forest type of the Andes.
16
Study Area
The forest type map of Peru (INRENA 1996) classifies the forests in the lower part of the
study area below 1 000 m a.s.l. as bosque humedo de colinas altas (bh ca = humid upper hill
forest). This forest type occupies only 1.4 % of the territory of Peru. Typical slopes are between
15° and 35° in variable topography. Access is generally difficult, maximum tree heights are about
35 m and distinct forest storeys are visible. The canopy height is low on steep slopes and on
ridges. The upper part of the study area, including the Cerro Tambo and the Cordillera
Cahuapanas, is classified as bosque humedo de montañas (bh mo = humid montane forest). This
type of forest covers 11.7 % of the national territory and basically extends entirely over the
eastern flanks of the Peruvian Andes up to 3 200 m a.s.l. in elevation. The relief is typically
mountainous and serrated by a web of rivers. A great number of species and life forms
(epiphytes, herbs, lianas, bushes and trees) occur in this region (Fig. 2.7).
2.1.6 Photographic Survey of the Study Region
Fig. 2.8: View to the south east from the Cerro Tambo into the Alto Mayo Valley.
(UTM 18S: 249400 / 9368339)
Clearly visible are the closed vegetation cover, the light color of the Cretaceous Cushatabay sandstones at
landslide edges in the foreground and the extensive patches of cleared ground for rice cultivation on the Alto Mayo
plains in the background.
Fig. 2.9: Abandoned agricultural clearing at the foot Fig. 2.10: Recently burnt vegetation on a ridge east of
of the Cordillera Cahuapanas near the Río
the Río Avisado showing clear signs of
Huascayaquillo. (UTM 18S: 262853 / 9373012)
onsetting erosion. (UTM 18S: 260549/ 9366109)
U(
MT
18
S
24:
90
99
93/
67
96
9)
Fig. 2.11: Panoramic view across the study area towards the ENE from a pre-summit of the Cerro Tambo.
The left half covers the Río Tioyacu watershed and includes the highest point of the Cordillera Cahuapanas (Pico de Cahuapanas 1 841 m a.s.l.) whereas the right half is dominated
by the Río Avisado watershed and the upper hill region.
17
Rice cultivation (Oryza sativa) is mostly done by hand using gravity irrigation techniques. Center look direction is towards the South.
Fig. 2.13: Panoramic view over extended rice fields on the bottom of the Alto Mayo Valley north of the village of Valle de la Conquista.
U(
MT
18
S
25:
69
77
93/
51
58
7)
Clearing is done by logging and burning of the vegetation to provide ground for the cultivation of coffee (Coffea arabica).
U(
MT
18
S
25:
78
76
93/
55
03
7)
Study Area
Fig. 2.12: Panoramic view to the ESE of a recently cleared patch of land on a gentle slope in the lower hill area.
18
Field Data
2.2 Field Data
The data for this study were obtained from August to September 2001 in cooperation with
staff from the Proyecto Especial Alto Mayo (PEAM) and students of the Universidad Nacional de
San Martín, Facultad de Ecología, Moyobamba.
2.2.1 Plot selection
The dissected upper hill area with its steep slopes, fern thickets, and swampy plateaus and
ravines restricts the accessibility of the study site to riverbeds and previously existing trails. These
trails connect the isolated clearings of Huascayacu, Paz y Esperanza and Gamínedes with each
other and with the marginal road, Yuracyacu - Pueblo Libre, and the banks of the Río Mayo. A few
simple trails leading further north towards the cordillera are maintained by indigenous tribes and
local hunters. Forty-five plots were chosen mainly along these trails for reasons of accessibility. A
representative distribution of plots was selected to cover all the geological formations of the
watersheds as displayed in the geological map derived from literature (c.f. Fig. 2.5 in chapter
2.1.3). For that purpose, three plots were arranged along an altitudinal gradient at the headwaters
of the Río Tioyacu (plots 1 - 3; for plot coordinates see Appendix 9.4.1) on the early Cretaceous
Cushatabay formation while nine plots covered the late Jurassic Sarayaquillo formation (plots
34 - 42), all primarily on sandstones. Seventeen plots were positioned over Quaternary sediments
from the Pleistocene and Holocene periods respectively (plots 4 - 17 and 43 - 45), also primarily
sandy substrates. For plot selection, emphasis was laid on distributing plots in all topographical
positions, e. g. ridge, ravine, valley bottom, and upper / lower slope. Unique and visually distinct
vegetation types (e. g. heath lands, fern thickets) were investigated more thoroughly. Plot
selection was done by a priori classification of the study site based on available geological
information, whereas in the field investigation, sites were chosen based on topographical
positions and observations. However, actual plot centers were chosen at random. Sites of evident
anthropogenic influence through logging or reported burning were excluded from observation.
Plot sizes differed according to the parameters and the type of vegetation being investigated.
Below, Tab. 2.2 illustrates the criteria applied to the respective situations and approaches.
Field Data
19
Tab. 2.2: Plot sizes for individual approaches: biometry, biomass harvest, structure and soil assessment.
Approach
Biometry
Biomass Harvest
Structure*
Soil**
Forest
Heath Land
Fern Thicket
Relascope
Relascope
Relascope
-
ø4m
2m×2m
25 m × 25 m
5m× 5m
15 m × 15 m
1 pit
1 pit
1 pit
* = Average values (line of sight)
** = Various depths
2.2.2 Recorded Parameters
Topographical, structural, biometrical, and soil parameters were assessed by identical
methodology for all plots. Mineral soils of plots encountered with a high water table could not be
sampled (plots 24, 25, and 27 - 29). Acaulescent individuals of the palm Jessenia bataua
(Arecaceae) occurred as a common species especially in ravines. Since tree biometry for
biomass calculations includes stem diameter and tree height as key parameters, these palms
with a thick base of dead leaves would greatly exaggerate the actual biomass. Therefore, a
special formula had to be developed in order to adequately consider the contribution to biomass
by this species. Three representative leaves were chosen for biomass determination in order to
establish a characteristic allometry allowing an improved biomass estimation for this acaulescent
palm species which occurred in all tree layers. Additionally, a biomass harvest was conducted on
plots where a dense fern layer covered the ground or where the majority of woody individuals did
not reach above 1.3 m. This was the case in the heath type vegetation (plots 11, 12 and 20 - 31).
2.2.2.1 Site Characteristics
Each plot was documented and characterized by general topographic parameters (Tab. 2.3)
as suggested e. g. by RICHARDS et al. (1940).
Plot elevations were determined in the field using two methods. First, measurements were
taken with a Global Positioning System (GPS) device (Garmin 12 XL). However, the
manufacturer suggests a vertical accuracy of its devices of ± 35 m (HTTP://WWW .GARMIN.COM/
SUPPORT/FAQS/8.HTML
April 2002). This data range is not satisfactory in order to characterize the
elevation situations within the small scale relief of the study area. Secondly, due to the lack of
reliable georeferencing points for calibration, elevations were post-determined by processing
barometric data recorded by two altimeters (Thommen Classic, Switzerland, 6 000 m and
9 000 m). Barometric data were assessed on site and referenced against values interpolated from
five-hourly measurements in Moyobamba (mean distance from investigation plots 43 km).
Elevation was calculated using a modified barometric elevation formula by STULL (1995) which is
elaborated in equations (1) and (2) (Appendix 9.1.1), Moyobamba was set for 880 m a.s.l. and
average temperature for 21 °C.
20
Field Data
Tab. 2.3: General site specific parameters.
Category
Parameter
General
Plot number
hh:mm
Begin of recording
2
m
Cloud cover / precipitation
Observation
square or circular
Measuring tape / Sonin ComboPro
Names
#
Camera used
Minolta x500 / Yashica 118
Frame
#
Film used
Agfachrome 100 ASA
Compass bearing
Suunto KB 14/360°
-
Cross section drawn schematically
Pencil
Location of plot
-
Placement relative to referece points
Observation
Coordinates
m
UTM, Zone 18 S, Datum WGS-84
Garmin 12XL
Sketch
°
Elevation GPS
m a.s.l.
GPS reading
Garmin 12XL
Elevation Altimeter
m a.s.l.
Altimeter reading
Thommen TX 23 9000m
Exposition
°
Compass bearing
Suunto KB 14/360°
Inclination
%
Atmospheric pressure
hPa
Inclinometer reading of slope
Suunto PM-5/1520
Barometer reading
Thommen TX 23 9000m
Position on slope
-
top / middle / bottom / ridge / ravine
Observation
Micro Relief
-
even / irregular / very undulated
Observation
Brief description of aspect
Disturbances
Stratification
-
Film number
Look direction
Stand
#
Time
Observer(s)
Topography
Equipment
dd/mm/yy
Plot size
Location
Annotation
Date
Weather conditions
Documentation
Unit
-
Observations, life forms, stand conditions Observation
Obvious human or natural disturbance
Dead wood
3
m
In the line of sight (ground / standing)
Observation
Estimation
Canopy density
%
Mean of fourfold coverage readings
Estimation
Brief description of layers
-
Anomalies, floristic description
Observation
Number of layers
#
Herb / shrub / 1-3 tree
Observation
Layer properties
-
Diffuse / clearly separable
Layer height
m
Layer cover
%
Observation
Estimation
In steps of 10 %
Estimation
2.2.2.2 Soils
At each plot one soil pit (approx. 0.5 m × 0.5 m, 0.4 - 1.3 m depth) was dug in order to assess
edaphic conditions. A partially superficial water table obstructed sampling of mineral horizons on
plots 24, 25, and 27 - 29. Soil horizons were identified, measured and described using German
soil nomenclature (AG BODEN 1996). Samples for chemical and soil-physical analyses were
seized for each individual horizon. The pH value of each mineral horizon as well as ground water
where available was measured using MERCK Spezialindikator pH strips (pH 2.5-4.5 / 4.0-7.0). A
total of 160 horizons were sampled. Bulk density was determined discontinuously where possible
with a steel cylinder of 0.1 dm 3 volume. Particle-size distribution was derived from texture
classified in the field according to AG BODEN (1996).
For chemical analysis YASIN (2001) suggests extraction with NH4NO3 (ZEIEN & BRÜMMER
1989) for extractable cations (Ca, Mg, K, Na, Al, Fe, Mn) in acidic soils of montane rainforests in
southern Ecuador in order to account fully for potentially high Al concentrations upon analysis by
flame atomic-absorption spectroscopy (AAS). Plant-available phosphorus was extracted with
Field Data
21
Tab. 2.4: Soil parameters from field and laboratory analyses.
Category
Parameter*
Field
Analyses
Ground cover
%
Parent rock
Name of horizon
Depth of horizon
cm
Texture
Aggregates
Color
Rock content
%
Rooting intensity
0-2
0-2
Organic matter content
0-2
Biological activity
Hydromorphic characteristics 0 - 2
pH units
pHH20
Laboratory
Analyses
Unit
Soil density (± 0.1g)
pHNH4NO3
(± 0.01)
Annotation
Equipment
Estimated in steps of 10 %: bare, litter, moss / lichen
If recognizable
Field name
KA 4
Measurement
Measuring tape
KA 4
KA 4
Munsell color chart
Rock volume estimated in steps of 10 %
After AG BODEN (1994): W0=(0), W1-3=(1), W4-6=(2 KA 4
Estimated from missing (0) to high (2)
Measured in distilled water (v/v approx. 1 : 5)
pH indicator strips, Merck Co.
Reference
AG BODEN (1996)
AG BODEN (1996)
AG BODEN (1996)
AG BODEN (1996)
-
kg dm-3
Soil cylinder samples (0.1 dm3) dried at 105 °C
pH units
Measured in extracted solution
-
Ohaus HP320
Ctot
(80 µg absol.)
%
CHN-Element-Analyzer
Ntot
(10 µg absol.)
%
CHN-Element-Analyzer
P
(0.50)
mg L-1
NaHCO3-Extraction, ICP-AEP-Detection
OLSEN & SOMMERS (1982) modif.
Ca
(0.50)
mg L-1
NH4NO3-Extraction, AAS-Detection
ZEIEN & BRÜMMER (1989) modified
Mg
(0.20)
mg L-1
after YASIN (2001)
K
(0.08)
mg L-1
Na
(0.50)
mg L-1
Al
(0.05)
mg L-1
Fe
(0.02)
mg L-1
Mn
(0.50)
mg L-1
-
* = accuracy and detection limits in parentheses
NaHCO3 (OLSEN & SOMMERS 1982, modified) and consequently analyzed using inductivelycoupled plasma-atomic emission spectroscopy (ICP-AES) as demonstrated by YASIN (2001).
Total carbon and nitrogen were determined by a CHN-Element-Analyzer. All chemical analyses of
soil samples were performed at the laboratory of the Analytic Center of the University of
Bayreuth, BITÖK. For a complete list of all soil parameters studied see Tab. 2.4.
2.2.2.3 Vegetation Structure
A structural classification of the vegetation was attempted since a floristic classification of the
study area was not feasible due to the lack of a complete and applicable local flora and time. The
term “vegetation structure“ shall be defined as the spatial distribution of vegetation parameters on
a stand level. Building on work by AXMACHER (1998), BENZ (1999), and PAULSCH & CZIMCZIK
(2001) from montane rain forests of Ecuador, a set of parameters and structural properties was
developed which were identifiable in the field (c.f. Tab. 2.5).
This set consists of both area based classification systems like the ones introduced by
RICHARDS et al. (1940) and W EBB et al. (1970) and individual plant systems by W ERGER et al.
(1982). In order to compensate for problems which arise from applying statistical analysis to
stand structural data from stands with different layering (BENZ 1999), all stands were separated
into a maximum of three tree layers which added up to total stand height. The set of parameters
22
Field Data
Tab. 2.5: List of structural parameters recorded in the field.
Parameter
Value
Scale level
Crown shape
Umbrella
Umbel
Globe
Cylinder
Palm tree
Cone
Irregular
Individual
Individual
Individual
Individual
Individual
Individual
Individual
Modified after WERGER & SPRANGER (1982)
Crown status
Entire
Inhibited
Crippled
Individual
Individual
Individual
BENZ (1999)
Crown contact
Solitary
Touching
Merging
Individual
Individual
Individual
BENZ (1999)
Individual
Individual
Individual
After WEBB et al. (1970) and
WERGER & SPRANGER (1982), modified
Plot
Plot
Plot
Plot
Plot
Plot
Plot
Plot
After RICHARDS et al. (1940) and
WERGER & SPRANGER (1982), modified
Plot
After RICHARDS et al. (1940), modified
Ramification
Leaf shape
Unbranched up to
Unbranched up to
Several stems
Oval
Lanceolate
Elongate
Round
Palmatifid
Palmatisect
Bipinnate
Other
cm
2
4-50 cm
2
50-225 cm
2
> 225 cm
2
0-4
Leaf Size
Vines / lianas
Epihytes
⅓ of total height
⅔ of total height
Reference
Plot
Plot
Plot
Abundance
On stem
In crown
Curtain
Ordinal
Plot
Plot
Plot
After RICHARDS et al. (1940),
modified after VARESCHI (1980)
Abundance
On stem
On branches
Ordinal
Plot
Plot
Ordinal
Plot
Plot
Mosses / lichens Abundance
On stem
In crown
was obtained separately for each of the three layers (E1 = upper, E2 = intermediate, E3 = lower
layer) estimating on an interval scale in steps of 10 % coverage for plot specific assessment and
10 % share for individual specific assessment. Abundance of special life and forms and taxa
(vines, lianas, vascular epiphytes, mosses, lichens) was evaluated using an ordinal scale in three
increments simplified after BENZ (1999), CZIMCZIK (1999), BÖRNER (2000), and PAULSCH (2001):
0 no
individuals
1 some individuals
2 many individuals
Field Data
23
Tab. 2.6: Biometric parameters recorded in the field.
Category
Dendrometry
Tree position
Biomass
Parameter
Stand basal area
Unit
2
-1
m ha
Annotation
Equipment
By WZP (BITTERLICH 1984) using scales 1 and 0.5
BITTERLICH mirror type Relascope
Girth at breast height (GBH)*
cm
Measurement at 1.30 m height on all WZP selected trees 2 m measuring tape
Angle to tree base
Angle to tree top
%
%
For tree height calculation, measured with inclinometer
For tree height calculation, measured with inclinometer
Suunto PM-5/1520
Suunto PM-5/1520
Distance (± 0.01 m)
m
Measured between inclinometer and observed tree
Sonin ComboPro ultrasonic meter
Compass bearing of tree from plot center
Suunto KB 14/360°
Direction
°
Distance (± 0.01 m)
Field name
m
-
Measured between plot center and observed tree
Sonin ComboPro / measuring tape
From local vernacular nomenclature for selected species
-
Fresh mass green leaf tissue
kg
Total green leaf tissue harvested within plot boundaries
Fresh mass woody tissue
kg
Total woody tissue harvested within plot boundaries
Various spring scales (50 g - 5 kg)
Fresh mass subsample leaf
Fresh mass subsample wood
g
g
Subsamples taken at random
Subsamples taken at random
Ohaus HP320 (± 0.1g)
Ohaus HP320 (± 0.1g)
Various spring scales (50 g - 5 kg)
* = trees > 1.30 m
2.2.2.4 Biometry
For characterization of above-ground biomass distribution a representative number of trees
was selected by the angle count method (Winkelzählprobe, WZP) after BITTERLICH using a mirror
type relascope with metric scale CP (FOB m.b.H., Austria) using scale unit 1. For thin stemmed
stands (e. g. in the heath forests) on plots 2, 17, and 22 - 33, scale unit 0.5 was applied. The WZP
is an optical method for quick determination of the basal area and a size-weighted selection of
representative trees in forest stands (BITTERLICH 1984). On plots 11, 12, 02, and 21 all trees
within a radius of 5 m were recorded. Dendrometric data were acquired for all selected trees
(Tab. 2.6).
For descriptive purposes the location of each described tree relative to the plot center was
recorded (Tab. 2.6). A biomass harvest was conducted on sites where recorded trees presumably
did not constitute the main share of above-ground biomass as suggested by CATCHPOLE (1992).
Plot sizes were chosen according to Tab. 2.2. With the exception of palm trees no monocotyledon
plants were included in plot measurements. In the case of the acaulescent palm tree Jessenia
bataua (Arecaceae) wood and leaf tissue fresh weight was determined separately for both
fractions based on the assumption that moisture rates differ considerably. Subsamples were
taken at random and weighed on site (Tab. 2.6).
24
Data Processing and Analysis
2.3 Data Processing and Analysis
Raw field data were organized in spreadsheets and files and a multivariate statistical analysis
of parameters, plotting and cartographic display of results followed.
2.3.1 Data Processing
2.3.1.1 Soils
Soil particle size distribution was derived from texture classified in the field according to the
key modified after AG BODEN (1996) in Tab. 9.9, Appendix 9.2.1.4. Missing values for
gravimetrical density were compensated with average values from measured horizons of the
same type as shown in Tab. 2.7.
Soil element data from laboratory analysis were screened and assigned to each horizon. The
exchangeable H+ concentration was calculated from exchangeable Al3+ concentrations and the
pH value difference in the extraction solution before and after extraction using the C++ program
exchHions by PRENZEL (1995), from the Institut für Bodenkunde und Waldernährung at the
Georg-August-University of Göttingen. The effective cation exchange capacity (CECeff) was
calculated according to BAIZE (1993) as:
CECeff = Ca2+ + Mg2+ + K+ + Na+ + Al3+ + H+
Accordingly, the base saturation (BS) in percent was calculated as:
BS = (Ca2+ + Mg2+ + K+ + Na+) / (Al3+ + H+) × 100
All cation concentrations were expressed in cmol of cations per kg of dry fine earth
(cmolc kg-1). Concentrations, pH value and particle size distribution were then calculated
separately as sums for topsoil (O and A horizons) and subsoil horizons (up to 60 cm depth) using
weighted means over the respective horizon thickness.
Tab. 2.7: Mean densities derived from actual density measurements for soil horizon classes which were
applied to horizons that lacked data for gravimetrical density.
Horizon
Mean Density
Ah
Bhs
Bil
Bv
n Bv
Cv
0.78
1.25
1.19
1.20
1.53
1.32
25
Tab. 2.8: List of poorly and highly represented structural
parameters from the field study.
Layer
Parameter
Occurrence
%
plots
Relevance
for Analysis
Crown cylinder
Leaf palmatifid
Leaf round
Leaf palmatisect
Leaf shape other
Ramification basal
3
3
1
3
1
3
0
0
<5
<5
<5
<5
0
0
1-2
1-2
1-2
1-2
Eliminated
Eliminated
Eliminated
Eliminated
Eliminated
Eliminated
Crown umbrella
Leaf shape other
Crown shape irregular
Leaf elongate
1
2
1
1
5-10
5-10
> 95
> 95
3
3
42-45
42-45
Remained
Remained
Remained
Remained
1
> 95
42-45
Remained
2
Leaf size 4-50 cm
Pools per hectare of all elements were calculated for each sampled horizon by density and
thickness. Pools per hectare were then calculated separately as sums for topsoil (0 - 20 cm) and
subsoil horizons (20 - 60 cm) using weighted means over the respective horizon thickness. The
division was made according to BÖRNER (2000) under the assumption that the root concentration
dominates in the upper 20 cm of the mineral soil and therefore the maximum nutrient uptake
takes place in this compartment.
2.3.1.2 Vegetation Structure
Data describing vegetation structure was first screened for unusually under-represented or
over-represented parameters. Consequently parameters that only occurred in < 5 % of all cases
(≤ 2 plots) were eliminated from further analysis due to the limited information conveyed.
Parameters that occurred in > 90 % of all cases (≥ 42 plots) remained in the analysis as they were
all interval scaled parameters with highly variable values still bearing information regarding the
variance between plots (Tab. 2.8).
Ordinal values as for epiphyte, liana and moss / lichen abundance were transformed into
interval scaled values as follows:
0= 0%
1 = 30 %
2 = 60 %
26
Tab 2.9: Biomass distribution classes calculated for tree height and girth at breast height
as basis for statistical characterization of the investigated stands.
Class
Parameter
Unit
1
2
3
4
5
6
7
8
Tree Height
[m]
<2
2-5
5-10
10-15
15-20
20-25
25-30
>30
Girth at Breast Height [cm]
<10
10-50
>200
-
-
50-100 100-150 150-200
2.3.1.3 Biometry
Trees were selected for assessment by the relascope method generally using scale 1. For
plots with many thin stems scale 0.5 was used. These method allows an upscaling of the stand
basal area to square meters per hectare by the “Winkelzählprobe” (WZP) after BITTERLICH (1984)
which simply equals the amount of trees observed exceeding the width of scale 1 on the
relascope in addition to half the amount of all stems matching the width of relascope unit 1.
Growth density expressed in the amount of stems per hectare are computed from relascope
measurements in combination with diameter at breast height (dbh) values using equation (7) in
Appendix 9.1.4.
For tree height estimation the distance to the observed tree and angles to the tree base and
to the tree top were measured in the field. Using the triangulation method corrected for sloped
distance measurements (eq. (16), Appendix 9.1.5), actual height of each tree was determined.
Biomass distribution within stands was characterized by calculating the abundance of stems in
each class for tree height and girth at breast height as listed in Tab 2.9.
Tree Biomass
Biomass for trees was calculated from empirically derived allometric equations by OGAWA
(1965) from four different tropical forest stands in Thailand. These equations are based on data of
diameter at breast height (dbh) and tree height to produce biomass values for the categories
stem, branch and foliage. Initially, these equations require the input of the dbh of each individual
only. Tree heights are computed from dbh values by using individual factors for each forest stand
described (see equations in appendix 9.1.2). In order to test which formula would be the most
appropriate for the different stands, tree heights calculated from measured dbh using all four
equations by OGAWA (1965) were compared to tree heights derived from field data. Fig. 2.14
suggests that none of the equations is apt to thoroughly describe all trees sampled in the same
way. However, the equations by OGAWA were distinguished in order to characterize different
forest types. Different forest types were hypothesized to occur in the study area as well.
Therefore, it seemed necessary to pre-classify the plots into rough height classes which should
be easier to assign to a certain biomass equation by OGAWA (1965).
40
40
r2 = 0.6603
r2 = 0.6611
35
30
30
25
25
20
20
15
15
10
10
5
5
0
tree height measured [m]
r2 = 0.6603
35
27
0
0
5
10
15
20
25
30
35
40
0
5
tree height OGAWA stand 1 [m]
10
15
20
25
30
35
40
0
5
10
15
20
25
30
35
40
Fig. 2.14: Comparison of tree height yields from diameter at breast height (dbh) measurements using three
different equations by OGAWA (1965) plotted against actual tree heights derived from field
measurements.
Ogawa stand 1 approaches the 1:1 diagonal only in the upper ranges whereas it over estimates tree heights
especially in the lower range.
Ogawa stand 2 gets closest to the 1:1 diagonal line in the lower ranges whereas it tends to under estimate heights of
taller trees.
Ogawa stand 4 seems to over estimate all tree heights systematically based on the data from all assessed trees.
Measured trees were subsequently grouped in classes ≤ 20 m, 20 - 30 m and > 30 m and
again plotted against the OGAWA values for each tree in the respective group from all equations.
Fig. 2.15 displays the formulas which appeared to produce the most similar results to the original
values and which would therefore be the most appropriate to adequately describe the stand tree
biomass.
Consequently, all tree biomasses of plots featuring trees with a maximum height of ≤ 20 m
35
40
r2 = 0.6214
r2 = 0.1502
height measured [m]
20
r2 = 0.2130
30
35
15
25
30
10
20
25
5
25
15
0
0
0
5
10
15
20
25
0
0
15
20
25
30
35
0
25
30
35
40
Fig. 2.15: Tree height yields from diameter at breast height (dbh) measurements using three different
equations by OGAWA (1965) plotted against actual tree heights derived from field measurements in the
ranges ≤ 20 m, 20 - 30 m and > 30 m.
Ogawa stand 2 equation was subsequently assigned to all plots with a maximum measured tree height of ≤ 20 m.
Ogawa stand 1 equation was subsequently assigned to all plots with a maximum measured tree height of 20 - 30 m.
Ogawa stand 1 equation was subsequently assigned to all plots with a maximum measured tree height of > 30 m.
28
were calculated using stand 2 equation. Tree biomasses of plots featuring trees with maximum
height of 20 - 30 m were calculated using stand 1 equation. Tree biomasses of plots featuring
trees with maximum height of > 30 m were calculated using stand 3 equation.
Eventually the computed biomass was multiplied by the abundance per hectare of each
individual tree. The sum of all tree biomasses recorded on a plot expressed the total aboveground biomass.
Palm Biomass
As mentioned in chapter 2.2.2.4 the palm species Jessenia bataua was represented in many
plots in large numbers yet lacked a stem that was conform with the biomass equations above.
These plants comprised of many leaves that were made up of a strong rib and many dissected
leaf lobes and reached from their bases until the canopy top. In the field, each individual was
characterized as well by its girth at breast height and its overall height of the tallest leaf. Three
representative leaves of different sizes were harvested and weighed for biomass. Sub-samples
were taken for the determination of dry biomass in the laboratory. The following formula was
derived based on the data obtained (cf. eqs. (3) - (6), Appendix 9.1.3):
wL = h 2.4 ⋅ (0.33135 ⋅ gbh + 23.95449 )
with wL
= total leaf biomass in
h
= height of tallest leaf in
gbh = girth at breast height in
kg
m
cm
Harvested Biomass
Biomass was harvested on plots, where trees did not comprise the main proportion of the
above-ground biomass. This situation occurred where either a dense fern layer covered the
ground (plots 11, 12, 20 and 21) or where the majority of woody individuals did not reach above
1.3 m which was the case in the heath type vegetation (plots 22 - 31).
All ferns (Sticherus remotus and Gleichenella pectinata, both Gleicheniaceae) were
harvested on a plot of 2 m x 2 m, divided into green leaves and brown stalks, weighed and
sampled separately. Likewise, all woody individuals on heath forest plots (Ø 4 m) were separated
into wood and foliage fractions, weighed and sampled separately and scaled up to the hectare.
Biomass values for larger trees obtained on these plots by OGAWA-calculations were
subsequently added to the harvested biomass for total stand biomass.
29
2.3.2 Statistical Analysis
For better comparison with previous studies from the project, soil data and biometrical
parameters were first evaluated in separate statistical approaches according to BÖRNER (2000).
Eventually all parameters were combined for an analysis of the total data as demonstrated by
DEMPEWOLF (2000) and DIETZ et al. (2002).
All methods of statistical analysis, which were applied, are described in detail in BACKHAUS et
al. (1996) and BROSIUS (1999) for further reference. Nomenclature was adopted from these
authors and from SPSS software (cf. definitions in chapter 7).
Before the cluster analysis, all data were Z-standardized to remove the distortion by variables
measured in different dimensions (BROSIUS 1999). After standardization, all variables have a
mean of 0 and a standard deviation of 1.
2.3.2.1 Spearman’s Correlation Coefficient
The first method for the reduction of a dataset by statistical analysis was the screening of the
data for highly correlated parameters using Spearman’s correlation coefficient. This was done to
test for redundant information since one variable of a correlated pair should be expelled from
further analysis. Exclusion threshold was set for a correlation coefficient of r ≥ │0.9│.
2.3.2.2 Principal Component Analysis
The second method to reduce a dataset was to submit it to Principal Component Analysis
(PCA). Principal Component Analysis is a robust method which accepts even the input of highly
correlated variables which makes a screening for Spearman’s correlation coefficient
unnecessary. This analysis helps to reduce the complexity of a dataset to only a few synthesized,
mutually independent parameters which can be handled and interpreted more easily (BROSIUS
1999).
Input data for PCA must be scaled metrically or at least by intervals. Therefore, abundance
classes were translated into intervals. All available variables of a dataset were then tested for
their suitability to Principal Component Analysis. This was done by computing the “Measure of
Sampling Adequacy” (MSA) for each variable by SPSS software. All variables with
“unacceptable” to “mediocre” MSA below 0.7 were eliminated from further analysis (Tab. 2.10).
Like the MSA for a single variable the KAISER-MAYER-OLKIN (KMO) measure expresses the total
sampling adequacy of an entire set of variables for Principal Component Analysis. The same
ratings from Tab. 2.10 apply.
30
Tab. 2.10: Rating of “Measure of Sampling Adequacy” (MSA) for the suitability of a parameter for
Principal Component Analysis.
(KAISER & RICE in BACKHAUS et al. 1996).
< 0.5
≥ 0.5
≥ 0.6
≥ 0.7
≥ 0.8
≥ 0.9
"unacceptable"
"poor"
"mediocre"
"fairly good"
"respectful"
"marvelous"
MSA Range
Rating
Principal Component Analysis tries to explain the total variance within a dataset iteratively
with mutually independent variables (= factors) in the multidimensional space. As this procedure
is done iteratively the first extracted factor explains the majority of the variance, the second factor
explains the majority of the remaining variance and so on with decreasing portions of explained
variance. The eigenvalue is a measure for the amount of the variance which is explained by a
factor. As variables are Z-standardized to a mean of 0 and a standard deviation of 1 before
analysis each variable therefore has a variance of 1. Consequently, a factor with an eigenvalue
< 1 is not significant as it describes less variance within the dataset than there is within each
variable itself. For this reason only factors with an eigenvalue > 1 are considered (“KAISER
criterion”). In order to optimize the solution for interpretability by Hierarchical Component Analysis
it is attempted to apportion the eigenvalues more evenly to the factors by rotating them in virtual
space without changing the overall variance explained.
In order to characterize and interpret a factor post-hoc with respect to the initial variables the
factor loadings are commonly used. Factor loadings indicate the proportion to which each
variable contributes to a certain factor.
As mentioned above, the extracted factors can be considered to be new variables describing
the variance within all submitted variables in a set of cases (plots). In other words: A factor score
is the degree to which a certain plot is described by the extracted factors. Factor scores,
however, can only be determined by regressional techniques and are repeated as a case-factor
matrix by SPSS software.
2.3.2.3 Hierarchical Cluster Analysis
The Hierarchical Cluster Analysis (HCA) is a technique to visualize similarities and
dissimilarities by joining most similar cases (in our case the individual plots) into a decreasing
number of clusters on the basis of independent variables. Consequently, the datasets must be
previously cleared of all correlated variables. This was done by testing for Spearman’s correlation
coefficient. Factors from Principal Component Analysis are mutually independent by definition
and therefore suitable for HCA.
According to BACKHAUS et al. (1996) every Cluster Analysis is sensitive to outliers.
Consequently, a procedure for detection of such outliers was necessary. BACKHAUS et al. (1996)
recommend a cluster analysis using the Single Linkage method, which links individual objects
300
31
Fig. 2.16: Hypothetical curve of the number of clusters
plotted against the employed distance measure
(BACKHAUS et al. 1996).
250
Distance (Dissimilarity)
The suggested cluster solution is marked by a sudden
“elbow” in the line to the right of which the distance
(as a measure of dissimilarity) between each
additional cluster shows no more significant
increase. From this point the dissimilarity rises
sharply with decreasing number of clusters.
Based on this principle a solution with up to five
distinguished clusters would be justifiable in this
case.
200
150
Elbow (5 Clusters suggested as final solution)
100
50
0
5
10
15
20
25
30
35
Number of Clusters
with very different properties as single clusters to the dendrogram. The corresponding
dendrograms are documented in Appendix 9.2.
For the final Cluster Analysis after elimination of obvious outliers Ward’s method was chosen.
It uses a different algorithm for joining those objects that minimize the increase of scatter within a
group. This leads to the creation of homogeneous clusters (BACKHAUS et al. 1996). SPSS
recommends for this procedure as measure of proximity the squared Euclidean distance. The
elbow criterion was consulted for the decision on the amount of clusters for the final solution
(BACKHAUS et al. 1996). In this procedure the squared Euclidean distance is plotted against the
respective number of clusters. This plot allows the graphical detection of the point at which the
heterogeneity between the clusters increases which is indicated by a steep rise of the slope to the
left of the “elbow” of the plotted curve. At the same time, the heterogeneity within the clusters
remains minimal which is expressed by the relatively small and constant rise of the slope to the
right of the “elbow” (Fig. 2.16). Appendix 9.2 shows all dendrograms of the final clustering of data.
BACKHAUS et al. (1996) propose the calculation of a modified t-value for characterizing each
cluster (see eq. (17), Appendix 9.1.6). Positive t-values indicate an over-representation of a
particular variable within each group relative to the other values, negative t-values indicate an
under-representation. The higher the absolute t-value for a particular variable, the more this
variable contributes to the separation of one cluster from others. t-values were calculated for all
datasets of the different approaches, all of their clusters and all of their variables in order to
identify the variables that cause the separation of clusters. For the plots which had been identified
as outliers the means of their properties were compared with the respective means of the clusters
and the plots were reintegrated into the clusters if appropriate.
2.3.2.4 One-Way ANOVA Analysis
One-way ANOVA Analysis is a procedure to compare different groups by their means. This is
done in SPSS Software by computing means and variation of a certain variable within given
groups. These values are then weighted for varying group sizes and compared to the mean and
32
variance of the respective variable over all cases. Differences between groups are rated for
significance levels but not specified for the individual groups. So called post-hoc tests explicitly
reveal these significant differences between groups. By the STUDENT-NEWMAN-KEULS-test
subsets are formed at a significance level of α = 0.05. Groups represented in each subset are
arranged to be homogeneous, i. e. they do not differ significantly by their mean whereas groups
represented in different subsets can be distinguished from each other significantly. As groups are
commonly of unequal sizes harmonic means are used.
2.3.3 Image Processing
Two radar satellite images (JERS-1, L-Band, resolution 100 m) were available from 1996 and
1997. No significant patterns for the vegetation could be detected for the study area on these
coarsely resolved images. All examined LANDSAT scenes (acquisition dates from 1996 until 1999:
9/64 1997, 9/64 1999, 8/64 1996, 8/64 1999) showed considerable cloud cover over the study
area near the Cordillera Cahuapanas and could not be used for vegetation analysis.
Panchromatic, stereographic aerial photographs of the study region were available from October
17, 1992 at the Instituto Geográfico Nacional in Lima. Image quality was sufficient. However, the
acquisition date of 1992 did not reflect the current situation of clearings and vegetation
disturbance patterns (fire, landslides) nine years later.
2.3.3.1 Aerial Photography
The geo-referencing of an aerial photograph (IGN Lima; Roll 17: Strip 335: No. 65) was
performed in ENVI 3.4 using conspicuous landmarks as reference points which had been
described by GPS measurements in the field. This photograph was used to derive information for
common characteristics of classified vegetation units under the assumption that the current
vegetation situation resembles the one nine years ago.
2.3.3.2 Digital Elevation Model Processing
From a pair of stereoscopic aerial photographs (IGN Lima; Roll 17: Strip 335: No. 64 & 65)
with a stereoscopic overlap of approx. 60 % a digital elevation model (DEM) was created. It
covers approx. 2 300 ha of the sampled core section of the study site from the upper hill area to
the foot hills of the Cordillera Cahuapanas. Digitizing of the DEM was performed at the
Geographische Institut of the Friedrich-Schiller-University in Jena using an electronic mirrorstereoscope (P 33, Planicomp, Zeiss, Germany). The elevation of the visible surface was
digitized with a horizontal grid, mesh size 50 m; vertical resolution was approx. 2 m. The final
product is a surface model including the height of the vegetation which was the only visible
surface in the densely forested area.
33
From the DEM the slopes were computed using Geographic Information System (GIS)
software. In ArcView, 3D-Analyst (ESRI, Redlands, Ca., USA) a map of slope classes was
computed for the DEM section. The DEM, the slope class map, and an overview map are shown
in chapter 3.4.3 and in Appendix 9.3.2.
2.3.4 Software
Word processing was done using MS Word 2000. Graphs and tables were generated with
SigmaPlot 2000 and MS Excel 2000. Statistical analyses were conducted in SPSS 10. Images
were enhanced and processed using Adobe PhotoShop 6, ACDSee 3.0, GIMP 1.2.3 and
PanoramaFactory 2.0. Stereoscopic images were digitized with AutoCAD 13. Georeferencing for
scanned maps was done using Fugawi 2.17, for aerial photography and satellite images using
ENVI 3.4. Mapping was performed with ArcView 3.2.
34
Soil
Results
3 Results
3.1 Soils of the Study Area
Soils of the study area were sampled at each investigated plot and were subsequently
analyzed for texture and chemical properties. This was done to test the first null-hypothesis which
claims edaphic conditions to be homogeneous in the upper hill area. Data were used for
classification of soil status by statistical methods. TIESSEN et al. (1994) reports that in poor soils in
the Amazon basin 95 % of all roots are found in the upper 30 cm of the mineral soil. It was
demonstrated by BÖRNER (2000) that the rooting depth of trees in these tropical soils is confined
to the uppermost 20 cm of the mineral soil. As this study investigates correlative relationships
between soil and vegetation, it focuses on element and nutrient status of the topsoil (0 - 20 cm
depth) and subsoil compartments (> 20 cm depth). Conventional soil classification schemes (e. g.
Soil Taxonomy, SOIL SURVEY STAFF (1998) or KA 4, AG BODEN (1996)) include aspects like soil
genesis and the function of the parent material which are considered only supplementary in this
study.
3.1.1 Statistical Classification
For each plot, data on 108 soil parameters were obtained. The dataset was prepared to be
expressed through mutually independent variables by Principal Component Analysis.
Independent variables were a pre-requisite for a subsequent Hierarchical Cluster Analysis. First,
tests were performed to identify highly suitable parameters for Principal Component Analysis.
Variables with a computed “Measure of Sampling Adequacy” (MSA) of less than 0.7 are
considered “mediocre” on the KAISER scale (cf. Tab. 2.10 in 2.3.2.3) and were therefore excluded.
Tab. 3.1: Soil parameters and their “Measure of Sampling Adequacy” (MSA) expressing their suitability for
Principal Component Analysis. All parameters listed were selected to represent the individual plots
in a Principal Component Analysis.
The parameters are divided into three categories: general soil properties, nutrient status and exchangeable cations.
General
Macro-Nutrients
Parameter
Code
MSA
Parameter
pH topsoil
PH_TS
0.902
Sand topsoil
SAND_TS
Sand subsoil
Clay subsoil
Cations
Code
MSA
Parameter
C/N ratio topsoil
CN_TS
0.821
Al
0.820
Ctot conc. topsoil
CC_TS
0.697
Al
SAND_SS
0.859
Ntot pool subsoil
NP_SS
0.753
Al saturat. ratio topsoil
CLAY_SS
0.905
Ntot conc. topsoil
NC_TS
0.585
Al saturat. ratio subsoil
-
-
-
Ntot conc. subsoil
NC_SS
0.789
Al saturat. ratio total pit ALCE_TOT
-
-
-
K conc. subsoil
KC_SS
0.782
Fe
KP_20
0.877
Mn
+
+
-
-
-
K pool upper 20 cm
-
-
-
K pool subsoil
-
-
-
Mg
+
2+
conc. subsoil
Overall KMO-Value for 20 Parameters: 0.814 = "respectful "
Code
MSA
3+
conc. topsoil
ALC_TS
0.854
3+
conc. subsoil
ALC_SS
0.898
ALCE_TS
0.744
3+
3+
ALCE_SS
3+
3+
3+
0.783
0.777
conc. subsoil
FEC_SS
0.884
conc. topsoil
MNC_TS
0.706
KP_SS
0.752
-
-
-
MGC_SS
0.700
-
-
-
Soil
Results
35
Tab. 3.2: Eigenvalues of the three factors extracted by Principal Component Analysis.
The three mutually independent factors together explain over 81 % of the variance within the dataset. Factors
from Principal Component Analysis were extracted to be mutually independent, iteratively explaining a maximum
of the variance within a dataset in a multidimensional space. Since variables were Z-standardized to a mean of 0
and a standard deviation of 1 before analysis each variable therefore has a variance of 1. Consequently, a factor
with an eigenvalue of < 1 is not significant since it describes less variance within the dataset than there is within
each variable itself. For this reason only factors with an eigenvalue of > 1 are considered. Rotation of the factors
in space (with their relative position to each other maintained) apportions the explained variance more evenly to
the factors.
Initial Eigenvalues
Total
FACTOR
Variance
explained
Rotated Values
Cumulative
Total
Variance
explained
%
1
2
3
11.03
2.37
1.22
Cumulative
%
61.28
13.17
6.77
61.28
74.44
81.21
6.50
4.25
3.87
36.11
23.61
21.49
36.11
59.72
81.21
,
The initial dataset was reduced to the 20 parameters listed in Tab. 3.1. It can be seen that all
aspects of soil quality (e. g. texture, pH value, nutrients, and cation status) were still represented.
An achieved KAISER-MEYER-OLKIN (KMO) value of 0.814 for the factor model rated it “respectful”
according to KAISER & RICE in BACKHAUS et al. (1996). Three factors were extracted using the
KAISER criterion (BACKHAUS et al. 1996) and were subsequently VARIMAX rotated (see regression
values for factor scores in Tab. 9.2 in Appendix 9.2.1.1). These three factors together explained
more than 81 % of the total variance in the data set (Tab. 3.2).
Examination of factor loadings (Tab. 9.3 in Appendix 9.2.1.2) suggested that none of the
factors clearly represented a certain category of parameters. Sets of parameters which were
eventually used to describe differences between the soils groups could already be ascribed
vaguely to the individual factors: Factor 1 contained parameters such as sandy texture, pH value,
and nitrogen content which points to acidic, sandy soils with high humus content such as podzols
or peaty soils in the area. Factor 2 is dominated by nutrient parameters with aluminum values
being under-represented suggesting a connection to more base saturated, nutrient rich soils.
High factor loadings for clay and aluminum were found for factor 3. These three sets of
parameters were therefore most suitable to characterize the different soils of the study region.
120
Sq. Euclidian Distance
100
80
Fig. 3.1: Soil analysis: Number of clusters plotted
against the employed distance measure:
squared Euclidean distance (BACKHAUS
et al. 1996).
Faint Elbow
7 Clusters suggested
60
40
The initial cluster solution is marked by an
“elbow” in the line where the distance between
each additional cluster does not increase
significantly any more.
20
0
5
10
15
20
25
Number of Clusters
30
35
40
36
Soil
Results
Regression scores of the three factors which had been extracted by Principal Component
Analysis were subsequently submitted to a Hierarchical Component Analysis. Outliers were
identified by a Hierarchical Component Analysis using the Single Linkage method. Six plots were
excluded by this criterion (P01, 13, 39, 40, 41, and 42). Data for the remaining 39 plots were
clustered using Ward’s method. The “elbow” criterion was used to decide on the number of final
clusters. A maximum of seven clusters were justified (Fig. 3.1). However, only five clusters were
separated because the seven-cluster solution would have required a statistically confirmed
distinction within Cluster 1 and Cluster 5 which could not be achieved with the available dataset.
The clusters roughly belong to three super-ordinate groups characterized by their main
texture: very sandy, sandy and clayey. Investigation of common properties of soils in each group
was done by calculating t-values (BACKHAUS et al. 1996) for each soil parameter (see
Tab. 9.4 - Tab. 9.8 in Appendix 9.2.1.3 for tabled t-values). In order to reflect the individual soil
types the represented clusters were labeled Acidic Sand soils (subsoil sand > 80 % and topsoil
pH < 3.1), Litter soils (thickness of O horizons > 20 cm on slopes > 10°), soils with a dark Carbon
Hierarchical Cluster Analysis
Ward Method with Outliers reintegrated (encircled)
Mostly Sloped
Terrain
Sandy
Cluster 2
Cluster 3
Cluster 4
Litter
Soils
Aluminum / Clay
Soils
Carbon
Sub-Soils
P05
P13
P37
P04
÷
ò
ò
ò
ò
ò
ò
ò
ô
ò ú
ò ú
ò ú
ò ÷
ò
P15
P09
P42
P01
P06
P43
P14
P17
Cluster 1
Acidic Sand
Soils
P07
P36
ó ó ÷
ò
ò
ò
ò
ò
ò
ò
ó ó ù ó ó ó ÷
ò
ò
ò
ò
ò
ò
ò
ò
ò
ò
ò
ò
ò
ò
ò
ø
ò ù ó ó ó
ò
ò
ó ÷
ò
ò
ò
ø ù ò
÷
ò
ò
ò
ô
÷
ò
ø
ò ò ò
ò ô
ò ú
ò ÷
ò ø
ò ú
ò
P16
P45
P10
P02
P07
P41
P40
P39
P38
P35
P34
P44
P20
P03
P18
P08
P11
P07
ó ó ó ó ó ó ó ó ó ÷
ò
ò
ò
ò
ò
ò
ò
ò
ò
ò
ò
ò
ò
Clayey
ò
ò
ó
ó
ù ó ó ó ó ó ó ó ó
ó
ó
ø
ó
ò
ò
ò
ò
ò
ò
ò
ò
ø
ò
ò
ò
ò
ò
ò
ò
ò
ò
ò
ò
ò
ò
ò
ò
ò
ò
ò
ó
ù
ó
ó
ø
ò
÷
ò
ò
ò
ò
ò
ò
ò
ò
ò
ò
ò
ò
ò
ò
ò
ò
ò
ò
ò
ò
ò
ò
ò
ò
ò
ò
ò
ò
ò
ó
ù
ø
ò
ò
ò
ò
ø ó ù ó ò
÷
ò
ò
ò
ò
ô
÷
û
÷
ø
ô
ú
ú
ú
ú
ú
÷
÷
û
ú
ò ò
ò ò ò ò ò ò ò ò ò ò ò
ò
ò ò ø
P21
P12
P07
P07
P32
P26
P33
P22
P30
P19
P31
P24
P27
P23
P25
P28
ó ó ó ó ó ó ó ó ó
ø
ò
ò
ò
ò
ò
ò
ò
ò
ò
ò
ò
ò
Very òòòSandy
ò
ò
ò
ò
ò
ò
ò
ò
ò
ò
ò
ò
ò
ò
ò
ò
ò
ò
ò
ò
ò
ò
ò
ò
ó
ù ó ó ÷
ø
ò
ò
ò
ò
ò
ò
ò
ò
ò
ò
ò
ò
ò
ò
øò úò úò úò ô
ú
÷
ø
ú
ø
ò ò ò ò ò ô
ò ú
ò ú
ò ÷
ò
ò ú
ò
P29
Plot
0
5
10
15
20
25
+---------+---------+---------+---------+---------+
Level
Terrain
Cluster 5
Base Cation
Soils
Fig. 3.2: Dendrogram of soil clusters generated by Hierarchical Cluster Analysis using Ward’s Method.
Six outlier plots were re-integrated into the five clusters based on their similar properties. The outlier plots P01,
13, 39, 40, 41, and 42 of the initial statistical analysis are encircled.
Distinction of clusters was confirmed by statistical analysis (cf. t-values in Appendix 9.2.1.3 and ANOVA
statistics in Appendix 9.2.2.4) on the basis of topographic position and texture. Soil units are described by
common texture, and / or element characteristics
Squared Euclidean distances used in the analysis were rescaled to a maximum of 25.
Soil
Results
37
Subsoil horizon (subsoil Ctot concentration > 1.0 % on slopes > 10°), soils rich in Aluminum and
Clay (Al3+ saturation > 50 % and subsoil clay > 40 %), and soils rich in Base Cations and
nutrients (topsoil base saturation > 10 % and POlsen measurable in the subsoil > 10 ppm). The six
outlier plots were post-hoc assigned to the cluster to which they showed greatest similarity.
Fig. 3.2 shows the cluster combination on the modified dendrogram for the analysis. Outliers are
re-integrated into the pattern and marked by circles. All previous analytical steps can be
reproduced with dendrograms in Fig. 9.2 and Fig. 9.3 in Appendix 9.2.1.2.
3.1.2 Results of Soil Analysis
For each classified soil unit, descriptive statistics were calculated over all plots contained.
Characteristic soil properties were compared between soil units. Complete soil data are
documented in Appendix 9.5.1.
Texture composition was examined for the grain size fractions of sand, silt and clay of the
bulk soil (Fig. 3.3 for topsoil and Fig. 3.4 for subsoil). In some cases, variability within the average
pedon of a soil unit from topsoil to subsoil was greater than differences between individual soil
units. Generally, the proportion of the silt fraction decreased in the subsoil in favor of both sand
and clay. Acidic sand soils contained the largest amount of sand primarily in the subsoil, highest
sand content in the topsoil show the often strongly leached soils from the carbon sub-soil unit.
Litter soils as well as aluminum / clay soils had the highest clay fraction both in topsoil and
subsoil.
Observed pHH 0 values of 2.8 - 4.1 are generally low for all soils sampled (Fig. 3.5). Topsoils
2
are slightly more acidic throughout, probably caused by higher concentrations of organic acids.
This may also be the reason for litter soils having a slightly lower pH than all other non-sandy
soils. Very consistent pH values in this soil group could be caused by the thick litter layer
Soil Clusters
Soil Clusters
100
100
Sand
Silt
Clay
Proportion [%]
Proportion [%]
80
60
40
20
80
60
40
20
0
0
Acidic Sand
Litter
Al / Clay
Carbon Sub
Base Cations
Fig. 3.3: Topsoil texture composition of the
fractions sand, silt, and clay of the soil
units.
Acidic Sand
Litter
Al / Clay
Carbon Sub
Base Cations
Fig. 3.4: Subsoil texture composition of the
fractions sand, silt, and clay of the soil
units.
38
Soil
Results
Soil Clusters
Soil Clusters
5.0
50
topsoil
subsoil
topsoil
subsoil
4.5
40
C/N ratio
pH value
4.0
3.5
30
20
3.0
10
2.5
0.0
0
Acidic Sand
Litter
Al / Clay
Carbon Sub
Fig. 3.5: The pH values of the soil units.
Base Cations
Acidic Sand
Litter
Al / Clay
Carbon Sub
Base Cations
Fig. 3.6: Distribution of C/N ratio in the soil units
of the study area.
influencing the leachate to be similar for each soil. Acidic sand soils display significantly lower
topsoil pH than all other units. Subsoil pH, however, is relatively high. Highest subsoil
concentrations for calcium were also measured for the acidic sand soils (cf. Fig. 3.15).
The C/N ratios of topsoils and subsoils generally range between 10 and 20 (Fig. 3.6). Values
for topsoils vary little in comparison to corresponding subsoils. Exceptions are litter soils with an
unusually narrow C/N ratio in the subsoil both absolute and relative to the topsoil. The C/N ratio in
the acidic sand soil group is significantly wider. Subsoil C/N ratio varies greatly. This may be
influenced by restrictions in sampling of moist sites due to a high ground water table. Sample size
is small for this sub-unit. The C/N ratios around 30 rather reflect properties of fulvic substances
than of humic matter as it seems likely for all other soils which are better drained.
Total carbon contents (Ctot) are significantly higher in the topsoil than in the subsoil. No
significant difference was found soils with a carbon subsoil horizon where subsoil carbon content
is highest among all units (Fig. 3.7). Subsoil carbon content and total carbon pools for topsoil and
subsoil are lowest in litter soils. Although subsoil carbon contents for acidic sand soils are
average, topsoil concentration is highest of all. Low microbial activity resulting in low
mineralization rates under waterlogged and strongly acidic conditions may provide an
explanation.
Total carbon concentration and total carbon pools show similar trends (Fig. 3.8). Although
carbon concentrations are higher in topsoils, subsoil carbon pools are larger due to significantly
denser subsoil horizons. Exceptions are litter soils and acidic sand soils where differences
between topsoil and subsoil concentrations were large.
Soil
Results
Soil Clusters
Soil Clusters
120
6
0 - 20 cm
20 - 60 cm
topsoil
subsoil
100
Ctot content [t ha-1]
5
Ctot content [%]
39
4
3
2
80
60
40
20
1
0
0
Acidic Sand
Litter
Al / Clay
Carbon Sub
Acidic Sand
Base Cations
Fig. 3.7: Distribution of Ctot concentration in the
soil units.
Litter
Al / Clay
Carbon Sub
Base Cations
Fig. 3.8: Distribution of Ctot pools in the soil units.
The situation for total nitrogen (Ntot) is similar to carbon. Both elements are closely linked as
chief constituents of soil organic matter (SOM). The variability of the nitrogen concentrations is
very large (Fig. 3.9). Highest nitrogen concentrations are found in the topsoil of aluminum / clay
soils whereas lowest concentrations are in the subsoil of acidic sand soils. Acidic sand soils are
poorest in total nitrogen both in concentration and in pools. For carbon sub-soils highest nitrogen
pools were calculated (Fig. 3.10).
The nutrient status of the soils is reflected in exchangeable potassium (K+). Concentrations in
topsoils exceed those in subsoils (Fig. 3.11) but larger potassium pools are found in the subsoils
(Fig. 3.12). The extreme variability within the topsoils of aluminum / clay soils is notable.
Generally, exchangeable potassium pools are highest for the base cation soils (206 kg ha-1
topsoil / 591 kg ha-1 subsoil) and on the lower end for carbon sub-soils (114 kg ha-1
topsoil / 273 kg ha-1 subsoil). There is practically no exchangeable potassium stored in acidic
sand soils (26 kg ha-1 topsoil plus subsoil) probably due to their shallow dimensions.
Soil Clusters
Soil Clusters
8
0.30
topsoil
subsoil
0 - 20 cm
20 - 60 cm
0.25
-1
Ntot content [t ha ]
Ntot content [%]
6
0.20
0.15
0.10
4
2
0.05
0
0.00
Acidic Sand
Litter
Al / Clay
Carbon Sub
Base Cations
Fig. 3.9: Distribution of Ntot concentration in the
soil units.
Acidic Sand
Litter
Al / Clay
Carbon Sub
Base Cations
Fig. 3.10: Distribution of Ntot pools in the soil
units.
Values for each soil pit are means weighted for horizon thickness. Displayed are means and their standard deviation for each soil unit.
40
Soil
Results
Soil Clusters
Soil Clusters
0.6
800
topsoil
subsoil
0 - 20 cm
20 - 60 cm
K+ content [kg ha-1]
600
0.4
0.3
0.2
400
200
+
K content [cmolc kg
-1
soil]
0.5
0.1
0.0
Acidic Sand
Litter
Al / Clay
Carbon Sub
0
Base Cations
Fig. 3.11: Distribution of exchangeable K+ concentration in the soil units.
Acidic Sand
Litter
Al / Clay
Carbon Sub
Base Cations
Fig. 3.12: Distribution of exchangeable K+ pools in
the soil units.
The phosphorus (POlsen) shown supposedly accounts for the plant-available portion of soil
phosphorus. Detection limits for analysis were high. Phosphorus concentrations below 10 ppm of
fine earth could not be detected by the analysis tools therefore zero values from analysis for
phosphorus may in reality still be considerable in dense and thick horizons but would not be
accounted for in the data. No plant-available phosphorus was found in subsoils, therefore
Fig. 3.13 displays only topsoil P pools. In many topsoils, irrespective of any soil units, and in most
litter soils no phosphorus was detected which resulted in high variation within the soil units.
Nevertheless, a continuous rise in P supply from 9.7 to 27 kg ha-1 can be proposed for the series
carbon sub-soils, aluminum / clay soils to base cation soils. A plant-available phosphorus supply
of only 0.8 kg ha-1 was found for topsoils of acidic sand soils. This supports the hypothesis that
heath forests, which were exclusively found on acidic sand soils, are suffering from P-limitation
(OZANNE & SPECHT 1979).
Soil Clusters
Soil Clusters
10
60
topsoil
subsoil
Al3+ content [cmolc kg-1 soil]
POlsen content [t ha-1]
50
40
30
20
8
6
4
2
10
0
0
Acidic Sand
Litter
Al / Clay
Carbon Sub
Base Cations
Fig. 3.13: Distribution of plant-available POlsen
pools in the topsoil of the soil units.
Acidic Sand
Litter
Al / Clay
Carbon Sub
Base Cations
Fig. 3.14: Distribution of exchangeable
concentration in the soil units.
Al3+
Soil
Soil Clusters
0.5
10
0.0
Litter
Al / Clay
Carbon Sub
20
10
0.5
0
-10
Acidic Sand
Base Cations
Fig. 3.15: Topsoil contribution of the exchangeable portion of bases (K+, Ca2+, and
Mg2+) to the effective cation exchange
capacity (CECeff) of the soil units.
1.0
0.0
0
Acidic Sand
30
CECeff [cmolc kg-1 soil]
20
Concentration [cmolc kg-1 soil]
1.0
40
Ca2+
Mg2+
K+
CECeff
30
Soil Clusters
1.5
40
Ca2+
2+
Mg
K+
CECeff [cmol c kg-1 soil]
Concentration [cmol c kg-1 soil]
1.5
41
Results
Litter
Al / Clay
Carbon Sub
Base Cations
Fig. 3.16: Subsoil contribution of the exchangeable portion of bases (K+, Ca2+, and
capacity (CECeff) of the soil units.
Exchangeable aluminum (Al3+) concentrations were highly variable (Fig. 3.14). Highest
concentrations were found in topsoils of litter soils and aluminum / clay soils which may be due to
the higher clay content of the parent materials. The exchangeable aluminum concentrations in the
acidic sand soils were surprisingly low. A possible explanation might be that most aluminum is
chelated permanently to organic complexes at extremely low pH (SCHEFFER et al. 1998).
Soil nutrient status is also characterized by the base cation micro-nutrients calcium (Ca2+)
and magnesium (Mg2+). Fig. 3.15 and Fig. 3.16 show the mean cumulative concentration of the
base cations calcium, magnesium, and potassium for topsoils and subsoils. Base cation soils
show the highest values in both topsoil and subsoil with only calcium decreasing substantially in
the subsoil. The most prevalent cation in these soils is magnesium.
As a general trend, concentrations of plant nutrients in the study area are very low and show
a relatively uniform and proportional decrease in base cation concentration with depth. With the
exception of acidic sand soils and base cation soils where calcium or magnesium dominate
respectively, all other soil units contain potassium as main fraction. In carbon subsoil soils
calcium is under represented. Subsoils of the litter unit contain potassium only. Nevertheless, the
effective cation exchange capacity (CECeff) is highest for the acidic sand soils as exchangeable
proton (H+) contribution is high at such low pH levels.
Topographic effects were not included in the soil analysis but were analyzed and evaluated
subsequently (Tab. 3.3). Only inclination (slope) which is presumably a driving force for soil and
element displacement caused recognizable differences between the clusters. Acidic sand soils
were distinguished from all other units. Litter soils were significantly distinguished from base
cation and acidic sand soils by One-Way ANOVA-Analysis which uses a harmonic group mean.
42
Soil
Results
Inclination
Elevation
xposition
m
°
°
Soil Unit
Litter Soils
Carbon Sub-Soils
Al / Clay Soils
Base Cation Soils
Acidic Sand Soils
a
27
a
20
b
17
b
15
c
1
(10)
(15)
(8)
(12)
(2)
969
1009
1038
982
985
(15)
(108)
(74)
(38)
(15)
133
286
201
157
179
(147)
(75)
(69)
(72)
(111)
Tab. 3.3: Mean slope, elevation above sea level,
and exposition as used to evaluate the
topographical situation of the five soil
units.
Soil units are arranged by increasing
inclination. Standard deviations are given in
parentheses.
3.1.3 Description
The
following
chapter
briefly summarizes
and discusses
the most conspicuous
characteristics of the classified soil units and assesses their implications for plant growth.
3.1.3.1 Soil Units
Acidic Sand Soils
The most remarkable soils in the study area are combined in the acidic sand soil unit. These
soils are exclusively found on level plateau sites within the Quaternary-Holocene sediments of the
upper hill region. The sites are covered with distinctly low and open tropical heath vegetation
(SPECHT 1979). The substrate originates from bright white sand layers of approximately 2 - 3 m
thickness (cf. COOPER 1979). These soils may be subdivided into a) well drained, more profound
soils which mostly occur towards the rim of the plateaus and b) into poorly drained or
permanently waterlogged soils over a duripan horizon displaying peat-like properties. Previous
studies by ONERN (1982) suggest that the impenetrable duripan is caused by cementing
substrate of volcanic origin. The subsoil texture is clearly dominated by the sand fraction.
Although the topsoil shows significantly less sand this trend may be blurred by the effect of large
amounts of organic material in the topsoils. In field assessment of texture, organic material is
known to simulate clay. The soils of this unit are the most acidic found in the area. Topsoils reach
pH values down to 2.8 probably due to a high concentration of organic acids. Subsoils are less
acidic at pH values about 3.5. C/N ratios of both topsoil and subsoil are significantly higher,
around 30 and are a consequence of anaerobic and acidic conditions. High variability within the
unit is driven by different properties between dry and wet sites. Due to accumulation of organic
substances the carbon concentration is highest, but the low horizon densities result in
comparable Ctot pools to other soils. Nitrogen values in the topsoil are slightly below average.
Soil
Results
43
Generally the nutrient situation of acidic sand soils is extremely poor. Except for some
calcium in the topsoil there are very few nutrients available and negligible plant-available
phosphorus (0.8 kg per hectare) was found. Subsoil nitrogen and potassium were marginal. The
exchangeable aluminum was surprisingly low and is probably chelated with organic matter. If
volcanic material (as mentioned by the soil survey by ONERN 1982) was actually present in the
substrate, high amounts of aluminum and iron would be expected paired with little phosphorus
which would be permanently bound. Indeed, the soils contain little phosphorus and relatively high
amounts of iron. Because analysis was not conducted for the identification of the characteristic
fractions for allophanes the volcanic origin seems plausible but could not yet be confirmed.
Litter Soils
Litter soils were named after a thick layer of litter which accumulated under a dense cover of
Sticherus remotus and Gleichenella pectinata. These soils can only be found on upper slopes on
Quaternary-Pleistocene sediments of the upper hill area. The soils are characterized by a distinct
change in texture from a silty topsoil with little sand to a subsoil with a relatively high clay content.
Topsoil pH varies little between 3.5 and 3.7 which is probably an effect of a similar composition of
litter leachate under this homogeneous fern cover. C/N ratio in the topsoil is approximately 20.
Significant differences in C/N between topsoil (19) and subsoil (10) suggests that there is
probably no connection between processes in topsoil and subsoil. Exchangeable aluminum
concentrations are among the highest found in the area which may also be a reason why no
plant-available phosphorus was detected. Concentrations of exchangeable base cations were low
with potassium dominating especially in the subsoil horizons. All soils which were assigned to this
unit were strikingly different in appearance. Topsoils varied from dark colors caused by black
carbon to hydromorphic colors which was due to obstructed lateral flow. Similar soil genesis could
not be inferred but all soils were found in sites recently disturbed by either fire or gravitational
mass transport by landslides.
Aluminum / Clay Soils
Aluminum / clay soils occurred on all geologic formations studied. They were most frequently
found on the late Jurassic substrate at the foothills of the Cordillera Cahuapanas on almost all
topographical positions with the exception of the immediate river plains. One site each was
located on gently sloped terrain over Quaternary-Holocene sediments and over early Cretaceous
rocks. In the upper hill area on Quaternary-Pleistocene sediments these soils were confined to
ridges and upper slopes. Although these soils showed the highest clay content in the subsoil,
their topsoil texture varied. Base cation status was highly variable. The soil reaction was about pH
4, which is in the range of aluminum buffering, and the exchangeable aluminum concentrations
were high.
44
Results
Soil
Carbon Subsoil Soils
Soils of the carbon subsoil unit were characterized by a black subsoil horizon with high
concentrations of carbon. These soils were found on gently to moderately sloped terrain but
never on lower slopes or in valleys and ravines. No distinction was made as to whether the
carbon allocation had occurred either by illuviation as in podzols (which seemed likely in most
cases) or as topsoil horizons buried in situ. In general, these soils showed the highest sand
content in the topsoil which is a common prerequisite for the formation of podzols fostered by low
pH values of approx. 4. Not surprisingly, subsoil carbon concentrations were highest among all
soils sampled and the Ctot pools were highest due to fairly thick and dense subsoil horizons.
Because nitrogen and carbon are closely linked in soil organic matter, Ntot dynamics follow those
of Ctot. concentrations and pools of exchangeable potassium which is leached easiest were
lowest compared to all other soil units. Plant-available phosphorus was present but with approx.
10 kg per hectare very low. Also, the concentration of exchangeable base cations was lowest in
this unit both for topsoil and subsoil.
Base Cation Soils
Base cation soils were found almost exclusively on lower slopes and in depressions, other
locations were on river plains. All sites appeared to have a supply of nutrients through seepage or
fluvial deposition but great differences exist in the moisture regime of these soils. Well drained
sites of rather sandy texture on river plains are included as well as permanently waterlogged soils
on the bottom of ravines. These soils did not show any other conspicuous properties other than
being rich in nutrients. The aluminum concentration was quite low and nutrients such as
phosphorus and potassium were sufficiently supplied. Nitrogen values are similar to all other sites
sampled. In the topsoil the concentrations of exchangeable calcium and magnesium were high
which might suggest the occurrence of magnesium deficiency due to antagonistic effects during
mineral uptake.
3.1.3.2 Summary of Soil Analysis
The most distinct soil units in the study area supported vegetation formations with low and
open canopy like chamizales and shapumbales and appeared to be linked to certain geological
situations.
Geological Effects
The nutrient poor sandy plateaus which are covered by chamizales are restricted to the
Quaternary-Holocene formations of the study area. No distinction, however, was achieved by
statistical analysis between the waterlogged soils and the better drained and more profound soils.
Soil
Results
45
Alluvial sand deposits are subject to land slides during heavy rains, especially when
deposited over clay layers. This situation is common on Quaternary deposits in the hill region.
Land slides can be triggered by earthquakes (BAILLIE 1996), which occasionally occur in the study
area (cf. Tab. 2.1 in chapter 2.1.3). Shapumbales on litter soils are found exclusively over
Quaternary sediments where landslides regularly produce bare and fresh patches ready for
colonization. Burnt snags and black carbon in the topsoil were encountered in shapumbales
indicating fires occurring on these slopes. Following intense fires the protective vegetation cover
for soils on such steep slopes may be largely destroyed and give rise to soil degradation by
erosion and small scale bulk soil displacement. This fire hypothesis is supported by significantly
different pH values for topsoils and subsoils which may indicate the separation of young
pedogenetic processes in the mineral subsoil from a high activity in the organic compartment.
Together with an unusually thick litter layer the discrepancy between the C/N ratios is the only
common characteristic for all litter soils. This suggests that not soil type but rather disturbance
conditions cause the extensive cover by thick fern layers. All other soil units in the Río Avisado
area were found independent of geological conditions.
Topographic Effects
Topographic aspects seem important for litter soils which are assumed to follow landslides.
Slopes become unstable on slopes > 25° (THOMAS 1994). Therefore, these sites may appear
more susceptible to landslides. Higher exposure to solar radiation and drainage by lateral flow
may create drier stand conditions and favor fires. These slopes seem the most disturbance
prone.
Base cation soils were characterized by high concentrations of nutrients. The water regime of
these soils was not considered in the analysis, but these soils were always found in narrow
ravines and valleys as well as in fluvial plains near rivers where water dynamics are high. The
favorable nutrient supply found on these sites is typically accumulated by lateral flow from
upslope and maintained by stagnant water in the case of ravines or by seasonal fluvial deposition
near larger rivers.
Carbon subsoil soils are found in all geological and topographical positions. The criterion of a
common dark carbon subsoil horizon is of little significance for interpretation. Such humus rich
horizons can be found as a secondary carbon allocation by illuviation on poor sandy podzolic
soils or constitute a buried organic horizon with fresh overlying material deposited by erosion
processes. Only knowledge about these processes would allow an interpretation of these soils for
plant growth. The abundance of tree ferns on these sites (cf. Fig. 3.20 in chapter 3.2.2) can either
be attributed to poor nutrient conditions or indicates a young stand age.
46
Soil
Results
150
100
100
50
50
-1
Above-Ground Biomass [t ha ]
Soil Units
150
0
0
0
5
10
15
20
25
30
35
POlsen Pool topsoil [kg ha-1]
Acidic Sand
Litter
0
1
2
3
4
5
6
7
8
Ntot Content [t ha-1]
Aluminum / Clay
Carbon Subsoil
0
1
2
3
4
5
6
7
Al3+ Concentration [cmolc kg-1 soil]
Base Cation
Fig. 3.17: Relation between soil chemistry and biomass for the respective soil units.
Plotted are soil nutrient pools of plant-available phosphorus and total nitrogen, and the total concentration of
exchangeable aluminum (Al3+) against the biomass values calculated for the stands of each soil unit. Variability
for the values given is high throughout.
Findings of different soil texture suggest that texture drives the soil development on the
slopes of the upper hill area into podzols (carbon subsoils) or Al / clay soils. If this was true, then
the finely layered deposits would make it impossible to predict differences in soil from topography.
3.1.3.3 Statistical Evaluation
Soil clusters could be significantly distinguished and classified based on their texture and
element composition as Tab. 9.20 in Appendix 9.2.2.4 demonstrates. Parameters were examined
by One-Way ANOVA to find significant differences of their means over each soil unit. A
classification sequence was suggested in order to unanimously identify each cluster. This was
possible for each parameter set and implies that vegetation reflects the edaphic conditions of the
site.
With respect to biomass, a characteristic feature of vegetation types, soil units could only be
grouped into two vague groups: 1) stands of higher biomass (> 100 t ha-1) as yielded by carbon
subsoils, Al / clay soils and base cation soils, and 2) low formations of little biomass (< 50 t ha-1)
as found over acidic sand soils and litter soils (Fig. 3.17). A dependence of biomass on soil
nutrient status is suggested for both phosphorus and nitrogen. However, phosphorus was
distributed unevenly over the study area. Many mineral soils especially in the upper hill region did
not contain any detectable plant-available phosphorus. Variability for phosphorus values was
therefore very high. On the other hand, the biomass accumulation pattern with respect to the total
aluminum concentration and to aluminum toxicity was indifferent.
Soil
47
Results
Tab. 3.4: Comparison of means for all classified soil units in respect to thickness of topsoil, pH, and nitrogen,
potassium, and phosphorus pools and concentrations.
Ntot
pH
Thickness
pool
Acidic Sand Soils
Litter Soils
Al / Clay Soils
Carbon Sub-Soils
Base Cation Soils
concentration
-1
cm
topsoil
K2O*
topsoil subsoil
topsoil subsoil
pool
%
t ha
total
kg ha
topsoil subsoil
concentration
-1
-1
topsoil subsoil
P2O5**
total
pool
-1
cmolc kg soil
kg ha
topsoil subsoil
topsoil
conc.
ppm
topsoil
17
2.9
3.5
1.4
0.0
1.4
0.14
0.01
26
0
26
0.11
0.05
1
1
9
3.6
3.7
1.1
1.2
2.3
0.15
0.02
71
462
532
0.24
0.17
0
0
15
4.0
4.1
2.7
3.8
6.5
0.19
0.07
187
365
552
0.30
0.17
22
16
11
3.9
4.1
1.5
5.4
7.0
0.15
0.08
66
273
339
0.16
0.11
10
6
16
3.9
4.0
2.3
3.1
5.4
0.14
0.05
165
591
756
0.25
0.24
29
12
* = NH4 NO3-Extraction
** = NaHCO3- Extraction
Variation within the plots is generally extremely high which leads to the conclusion that soils
grouped according to this approach are not suitable to explain different vegetation patterns. Soil
units include stands which are not at similar developmental stages. The spectrum of forest types
stocking on soils within one particular unit is broad. For example base cation soils as well as
Al / clay soils both include very tall stands rich in biomass as well as short stands rich in
unproductive palms. They create a high variation of biomass characteristics within the soil unit
and therefore yield similar mean values for biomass of many soil units. Classifying stands based
on the soil units alone will mask the actual variation of biomass between stands and thus
obviously different vegetation types will not be distinguished.
An overview of mean pool values and concentrations for the macro-nutrients nitrogen,
potassium, and phosphorus in topsoil and subsoil of all soil units is given in Tab. 3.4.
48
Biometry
Results
3.2 Stand Biometrical Analysis
An inventory of biomass and dimensions of all woody individuals (> 1.3 m) which were
selected by the relascope method was conducted at each plot in order to test the second nullhypothesis. This hypothesis assumes a homogeneous distribution of stand biomass within the
study area. Basal area in the stands was assessed by the angular count method
(Winkelzählprobe, WZP) by BITTERLICH (1984). Trees were measured for height and stem girth at
breast height (1.3 m). Recorded individuals were also marked in the categories dead, inclined,
tree fern, palm, and liana. Species were named in the field by their local vernacular name.
From the data above-ground biomass was determined for each plot using equations by
OGAWA (1965) as specified in chapter 2.3.1.3. Stem abundance was calculated for each
individual tree. Classes were formed for tree height and girth in combination with abundance
data. These classes characterize patterns of the distribution of biomass and consequently are a
measure for vegetation growth patterns (cf. Tab 2.9).
The biometrical classes were examined for their correlations but no highly significant
correlations (> │0.9│) were found (Tab. 3.5). Thus, values from all computed biomass distribution
classes were submitted to a Hierarchical Cluster Analysis.
The “elbow” criterion was used in order to decide on the number of final clusters. A number of
six clusters was suggested (Fig 3.18), but after thorough analysis of the cluster dendrogram
eventually only five clusters were separated because the sixth cluster would have been made up
of plot P01 only (Fig 3.19).
Tab. 3.5: Spearman’s correlation coefficient matrix generated for all biometric classes calculated from
measurements.
No extremely high values (> │0.9│) were observed therefore all of the parameters shown were submitted to a
Hierarchical Cluster Analysis.
ba2_025
ba2_050
ba2_075
ba2_100
ba2_250
ba2_500
ba2_1000
ba2_2000
ba2_max
hitea02
hitea05
hitea10
hitea15
hitea20
hitea25
hitea30
hiteamax
18 Parameters used
0.23
0.04
-0.32
-0.33
-0.41
-0.39
0.31
0.15
0.08
0.34
-0.03
0.59
0.42
0.36
0.32
0.47
0.65
0.60
0.67
0.56
0.67
-0.61
-0.48
-0.48
0.56
0.58
-0.23
-0.02
-0.31
-0.32
-0.14
0.58
0.47
0.00
-0.13
-0.19
-0.16
0.14
0.69
0.24
0.15
0.10
-0.48
-0.07
0.63
0.44
0.18
0.13
-0.43
-0.17
0.70
0.52
0.18
0.12
-0.57
-0.06
0.72
0.58
0.56
0.53
-0.44
-0.29
0.53
0.72
0.69
-0.48
-0.46
0.35
0.78
-0.36
-0.61
0.05
-0.30
-0.56
0.01
0.25
-0.54
-0.02
-0.39
-0.49
-0.35
-0.28
-0.27
0.12
-0.17
-0.31
-0.29
-0.33
0.28
-0.06
-0.13
-0.08
-0.14
0.57
0.31
0.17
0.13
0.03
0.61
0.30
0.07
0.06
-0.04
0.67
0.44
0.21
0.08
0.04
0.78
0.68
0.41
0.30
0.27
0.70
0.81
0.59
0.49
0.37
0.56
0.87
0.77
0.68
0.58
0.53
0.76
0.51
0.57
0.51
-0.50
-0.44
-0.40
-0.31
-0.25
-0.36
-0.48
-0.49
-0.44
-0.41
0.62
0.20
0.02
-0.03
-0.08
0.72
0.32
0.22
0.11
0.71
0.60
0.48
0.83
0.71
0.86
hitea02
hitea05
hitea10
hitea15
hitea20
hitea25
hitea30
ba2_010 ba2_025 ba2_050 ba2_075 ba2_100 ba2_250 ba2_500 ba2_1000 ba2_2000 ba2_max
Biometry
49
Results
180
160
140
120
Elbow
100
80
60
Fig 3.18: Number of clusters plotted
against
the
squared
Euclidean
distance
(BACKHAUS et al. 1996).
40
20
0
5
10
15
20
25
30
35
40
45
Number of Clusters
When including canopy density Clusters 1 to 5 could already be distinguished roughly by the
parameters biomass and tree height. Two groups had an average canopy cover < 60 %. The first
of these groups featured taller stands with a maximum height > 5 m and was called “Open
Woodland” (Cluster 1) whereas stands which did not reach 5 m were assigned to the
“Shrubland” (Cluster 2). The group of plots with closed canopy (cover > 60 %) and tall trees
Cluster 1
Open Woodland
Short
Cluster 2
Cluster 3
Cluster 4
Cluster 5
Shrubland
Tall
Forest
Dense Forest
Short Forest
P09
P05
P41
P08
P13
P04
÷
ò
ù ó ó ÷
ò
ø
ò ù ó ÷ò
÷
ò ø
ò ô
ò ú
ò ÷
ò ûò ÷
ò
P45
P36
P19
P16
P06
P43
P39
P10
P15
P40
P37
P14
P07
ó ó ó ÷
ò
ò
ò
ò
ò
ò
ò
ò
ò
ò
ò
ò
ò
Regular
ò
ò
ó ó ó ù ó ó ó ó ÷
ò
ò
ò
ò
ò
ò
ò
ò
ò
ó
ó
ø
ô
ò
ò ó ó ó ó ó
ò
ò
ø
ò
ò
ò ó ó
ó ÷
ø
ò
ò
ò
ò ù ÷
ò
ò
ò
÷
ò
ø
ô
÷
û
÷
û
÷
ø
ò ò
ò ò ò ò ò ò ò ò ô
ò ú
ò ú
ò
P17
P02
P34
P35
P38
P18
P25
P01
P44
P42
P03
P25
P23
ó ó ó ó ó ó ó ó ó ó ÷
ò
ò
ò
ò
ò
ò
ò
ò
ò
ò
ò
ò
ò
Tall
ò
ò
ó
ó
ù ó ó
ó
ó
ø
ò
ò
ò
ò
ò
ò
ò
ò
ò
ò
ò
ò
ò
Dense
ò
ò
ò
ø
ò
ò ó
ò
ò
ò
ò
ò
ò
ò
ò
ò
ò
ò
ò
ø
ù
÷
ò
ò
ò
ò
ò
÷
ò
ò
ò
ò
ò
ò
ù
ò
ø
÷
ò
ò
ò
ò
ò ò
ò
ò
ò
ò
ò
ø
ó
ô
ò
ò
ò ò
ò
ò ú
ò
ô
÷
û
÷
ò
ò
ø
ô
÷
ò ò
ò ò ò ò
ò ò ò ò
ò û
ò
P32
P28
P31
P29
P25
ó
ú
ò
P25
ó
P24
P30
P22
P20
P11
P33
P26
P21
ø
ò
ò
ò
ò
ò
ò
ò
ò
ò
ò
ò
ø
ó
ó
ù
ò
øò ú
ú
ô
ú
÷
ò ò ò ò ò ø
ò
ø ó ó ó ó ó
ò
ò
ò
ò
ò
ò
ò
ò
ò
ò
ò
ò
ò
ò
ò
ò
ò
ò
ò
ò
ò
ò
ò
ò
ò
ò
ò
ò
ò
Short
ò
ò
ò
ò
ó ù ó ó ó ÷
ò
ò
ò
ò
ò
ò
ò
ø
ò ù ó
ò
ò
÷
ò
ò
ò
ô
ø
ò ÷
ò
ò ô
ò ÷
ò ø
ò
P27
Tall
Closed
Canopy
P25
Open
Canopy
P12
Plot
15
20
25
0
5
10
+-----------+-----------+-----------+-----------+-----------+
Ward Method, Z-Standardized Parameters
Fig 3.19: Dendrogram generated by Hierarchical Cluster Analysis using Ward’s method for the extraction
of clusters with maximum similarity.
Z-standardized values are used from 18 parametersdescribing the stand biometrical properties of 45 plots as
shown in Tab. 3.5.
Clusters names correspond to the stand types predominantly found in each cluster.
Distances used in the analysis were rescaled to a maximum of 25.
50
Biometry
Results
Tab. 3.6: t-values calculated after BACKHAUS et al. (1996) for all biomass parameters found in the individual
biometric clusters.
High t-values indicate a parameter being relatively over-represented within a group with respect to the entire
dataset. t-values (> │0.8│) are marked bold. Units are arranged according to increasing tree height.
t-Values
Tree Height
Girth
(breast height)
Class
Shrubland
Open
Woodland
<10 cm
10-50 cm
50-100 cm
100-150 cm
150-200 cm
>200 cm
2.23
-0.56
-1.13
-0.61
-0.54
-0.38
-0.07
-0.54
-1.12
-0.61
-0.54
-0.38
-0.23
1.71
0.69
-0.45
-0.54
-0.38
-0.49
0.01
0.52
0.23
0.48
-0.14
-0.55
-0.81
0.96
2.18
0.97
2.80
<2 m
2-5 m
5-10 m
10-15 m
15-20 m
20-25 m
25-30 m
>30 m
1.90
1.57
-0.69
-0.89
-0.62
-0.37
-0.31
-0.18
-0.01
-0.02
-0.60
-0.89
-0.62
-0.37
-0.31
-0.18
-0.40
0.04
1.83
0.26
-0.55
-0.37
-0.31
-0.18
-0.38
-0.41
-0.03
0.72
0.48
-0.20
-0.18
-0.18
-0.41
-0.54
-0.53
-0.11
1.26
3.03
2.58
1.84
Dense Forest Short Forest
Tall Forest
(maximum height > 30 m) was called “Tall Forest” (Cluster 3) whereas closed canopy stands of
lower maximum height (< 30 m) were distinguished by their growth density. “Dense Forests”
(Cluster 4) with > 5 000 stems per hectare are significantly denser than “Short Forests”
(Cluster 5) with < 5 000 stems per hectare.
All common properties within the stands of each group were analyzed by calculating t-values
(BACKHAUS et al. 1996) for each biomass distribution class (Tab. 3.6). The analysis of these
values showed that the shrubland vegetation is characterized by very thin and short individuals.
No domination of any parameter could be found in the open woodland unit which combines two
open but evidently different vegetation types. This class features merely an under-representation
of thicker stems (girth 50 - 100 cm) and higher trees (> 10 m) which is both similar to the shubland
unit. Only the very short and thin stems are missing. Forests of the dense forest unit are
dominated by thin stems (girth 10 - 50 cm) and short trees (5 - 10 m) whereas forests of the short
forest unit are characterized by slightly taller trees (10 - 15 m). No further unique characteristics
could be observed for this unit. As it is the largest group (n = 18) uniting several different plots it is
the least homogeneous showing highest variability between its constituents. Tall forests are
dominated by large trees throughout.
3.2.2 Results of Biometrical Analysis
For each classified biometric unit the means and standard deviations of all plots contained
were calculated. Characteristic properties of stand biometry were compared. The overall
biometric data are documented in Tab. 9.23 in Appendix 9.2.3.1.
Biometry
Results
Biometry Cluster
Biometry Cluster
250
35000
Tree Ferns
Standing Dead Wood
Palms
30000
25000
Woody Stems [ha-1]
200
Woody Individuals [ha-1]
51
150
100
10000
7500
5000
50
2500
0
al
RF
RF
mb
ed
ne Tall
apuDense
nta
rish Short
/ Sh
mo
ove
izal
Woodland
Forest Imp
Forest Pre
Forest
am
n se
l de
iza Open
am
Ch
Shrubland
Fig. 3.20: Means of non-tree woody individuals
(palms, tree ferns) and standing dead
wood plotted as individuals per hectare
for each biometric unit.
0
Shrubland
e
al
ens
mb
dOpen
uDense
hap
izal
am Woodland al / S
Forest
Ch
iz
RF
Short
ed
ri sh
ove
Forest
mp
RF
ne Tall
nta
mo
Forest
Pre
Fig. 3.21: Growth density means (expressed in
stems per hectare) plotted for each
biometric unit with standard deviations.
Woody individuals were recorded in four categories: Living trees, dead trees, palms and tree
ferns (stand characters) in order to readily describe specific site conditions. Dead trees were
excluded from biomass calculations because their wood density values and the contribution to the
total above-ground biomass was uncertain. Fig. 3.20 shows the spectrum of all non-tree woody
individuals including standing dead wood for each biometric unit. No tree ferns or palms were
encountered in the shrubland and open woodland units, both of which have a very open canopy.
However, these stands abound in standing dead woody individuals. The number of standing
woody individuals is also high in the dense forest unit but tree ferns occur there as well. Most tree
ferns and palms can be found in the short forest unit which has considerably less standing dead
wood than dense forests. This unit is comprised of a large number of plots mostly within the
upper hill area slopes and ravines, therefore variability is high. The number of palms may indicate
moister site conditions. For tall forests with fairly moist climate it is not surprising that both dead
wood as well as palm and tree ferns are present but only at low abundance since overall growth
density is fairly low in this unit (Fig. 3.21). The lowest overall density of living woody individuals
was found in tall forests and only the shapumbales in the open woodland unit can be compared to
tall forests with respect to growth density. Growth density is significantly higher in dense forests
and the highest density rates were found for shrubland with about 25 000 individuals per hectare.
Examination of tree growth patterns was done by plotting their diameter at breast height
(dbh) against the respective tree height. Fig. 3.22 shows the scatter of values from 1 209 woody
individuals. Most individuals were less than 20 m high and less than 30 cm thick at breast height.
Heights of up to 35 m were recorded with stem thickness varying from 30 to 80 cm. Individuals of
heights between 5 and 15 m covered the whole spectrum of stem thickness from 3 to > 80 cm. A
common relation was calculated at r2 = 0.63 but the overall scatter was great. Growth patterns in
terms of the relationship between tree height and stem size were similar as the fits showed
similar curvature (Fig. 3.23). However, stands could be divided roughly into three groups:
52
Biometry
Results
35
35
30
30
25
Tree Height [m]
Tree Height [m]
25
2
r = 0.63
n = 1209
20
15
10
20
15
10
Total Trees regression
Shrubland
Opern Woodland
Dense Forest
Short Forest
Tall Forest
5
5
0
Shrubland
(r 2 = 0.30 n = 111)
Open Woodland
(r 2 = 0.42 n = 131)
Dense Forest
(r 2 = 0.46 n = 299)
Short Forest
(r 2 = 0.45 n = 528)
Tall Forest
(r 2 = 0.58 n = 144)
0
0
20
40
60
80
Diameter at Breast Height [cm]
Fig. 3.22: Regression fitted to the diameter at
breast height vs. tree height plot for all
1 209 assessed trees > 1.3 m.
0
20
40
60
80
100
Diameter at Breast Height [cm]
Fig. 3.23: Regressions fitted to the diameter at
breast height vs. tree height plots for
each biometric unit.
Shrubland and open woodland have low vegetation with fairly thick stems. Dense and short forest
types developed taller trees at similar stem size. Tall forests had the tallest trees at similar dbh
with dense and short forest but lacked very thin stems in their stands.
Mean diameter at breast height for all biometry units rises from the shubland (2.9 cm)
continuously to tall forests (30.9 cm). Fig. 3.24 suggests that stem sizes (expressed as girth at
breast height) for the dense forest remained quite uniform whereas the short forest showed very
high variation. The fact that the short forest unit also includes all ravine forests which are rich in
thick palms, like Jessenia bataua, may account for this high variability. A similar trend to mean
stem sizes can be seen for tree heights of all biometric units (Fig. 3.25).
In order to visualize the distribution of biomass in terms of stem size the basal area was
computed for each woody individual assuming circular cross-sections for each stem. Individuals
were then divided into nine groups. According to Fig. 3.26 both shrubland and open woodland
units abound in the smallest girth classes with the shrubland still significantly exceeding the open
woodland with 22 900 vs. 4 100 stems per hectare of < 10 cm 2 basal area. Stem abundance for
the dense forest also peaks in the smallest two basal area classes, yet this stand type features
trees of all sizes and actually shows most trees of 100 - 200 cm2 basal area over all units. Similar
proportions were calculated for the short forest. Still, trees were distributed more evenly over all
classes with the smallest class being far less dominant. In tall forests barely any thin trees were
found and the overall stem abundance in tall forests was low and concentrated in the classes of
25 - 50 cm 2 and 100 - 250 cm 2 basal area.
Biometry
Biometry Cluster
Biometry Cluster
25
120
100
20
80
Tree Height [m]
53
Results
60
40
15
10
5
20
0
Shrubland
am
Ch
al
mb
uDense
hap
nse
Open
de
izal
Woodlandizal / S
am
Ch
Forest
RF
edShort
rish
ove
Forest
Imp
RF
ne Tall
nta
mo
Forest
Pre
Fig. 3.24: Girth at breast height means plotted for
each biometric unit with standard
deviations.
0
Shrubland
bal
n se
Dense
deOpen
pum
izal
Sha Forest
ham Woodland al /
RF
Short
hed
eris Forest
pov
RF
ne Tall
onta Forest
rem
Fig. 3.25: Tree height means plotted for each
biometric unit with standard deviations
A similar approach was taken for tree heights (Fig. 3.27). Eight height classes were formed.
Shrublands were dominated by individuals < 2 m (13 900 individuals per hectare) whereas open
woodlands had most individuals between 2 and 5 m in height. In both units no trees > 10 m were
recorded. In the dense forest the tallest trees were 15 - 20 m, trees 5 - 10 m were most abundant,
and trees < 2 m hardly existed. In the short forest the tallest trees reached heights of 25 - 30 m,
but most trees (especially palms) were only 5 - 10 m high. In tall forests no trees < 2 m were
recorded and trees of heights 5 - 20 m were quite evenly distributed. Tallest individuals exceeded
30 m in height.
A measure integrating stem abundance and density is the basal area for an entire stand
(Fig. 3.28). On this basis only shrubland and open woodland (< 10 m 2 per hectare) could be
distinguished significantly from all other units (> 20 m2 per hectare).
22 90
0
1 3 90
0
10 20
0
4 100
4000
m
r of Ste
Numbe
-1
[ha ]
5000
2000
3000
0
d
Shrublan
oodland
Open W
Forest
Dense
res
Short Fo
250
t
res
Tall Fo
>
t
- 500
00
rea
10
500 1000
Fig. 3.26: Distribution of stems into basal area
classes for the classified biometric units.
2 -5
5 - 10
1000
10 - 1
20
15 -
0
nd
Shrubla
oodland
Open W
Forest
Dense
res
Shor t Fo
20 0
20 - 3
t
> 30
5
[m
]
50 - 75
0
75 - 10
250
100 -
0 -2
2000
las
s
1000
He
i gh
tC
Cla
ss
[ cm 2
]
0 - 10
25
10 25 - 50
Ba
sa
lA
r of Ste
Numbe
-1
]
ms [ha
3000
25
st
Tall Fore
Fig. 3.27: Distribution of stems into tree height
classes for the classified biometric units.
54
Biometry
Results
Biometry Cluster
30
20
10
0
bal
nse
Dense
um
deOpen
hap
izal
am Woodland al / S
Forest
Ch
z
Shrubland
Biometry Cluster
300
Biomass above Ground [t ha-1]
Basal Area [m2 ha-1]
40
RF
Short
hed
eris Forest
pov
200
150
100
50
0
RF
ne Tall
onta Forest
rem
Fig. 3.28: Basal area means with standard
deviations of the biometric units.
250
Shrubland
h am
nse
l deOpen
iza
Woodland
al
Dense
mb
apu
Sh Forest
al /
RF
Short
hed
eris Forest
pov
RF
ne Tall
onta Forest
rem
Fig. 3.29: Standing above ground biomass means
with standard deviations of the
biometric units
Total above-ground biomass was calculated as the most comprehensive parameter for each
stand (Fig. 3.29). Shrubland and open woodland were lowest in biomass (both < 50 t per
hectare). The dense and short forests showed values quite similar to each other (slightly > 100 t
per hectare), but variability was high within the short forest unit. The variability can be ascribed to
an abundance of palm trees in many ravine sites which are relatively low in biomass. Biomass of
the tall forest generally exceeded 250 t ha-1.
3.2.3 Description
The
following
chapter
briefly summarizes
and discusses
the most conspicuous
characteristics of each classified biometric unit and their relation to soil nutrient status.
3.2.3.1 Biometry Units
Tall Forest (Valley)
Tall forests were confined to the level river plains and terraces found in wider valleys. These
valleys were formed by larger rivers than the one existing within the upper hill area. The tallest
individuals (40.5 m) as well as some of the thickest trees (diameter 70 cm) were found in these
forests whereas very short individuals (< 2 m) were rare and only very few thin stems (< 25 cm2
basal area) were recorded. This indicates competition for light under favorable growth conditions
in terms of slope, moisture regime, and nutrient availability. In spite of the lowest stem density
overall (averaging 1 404 stems per hectare), the highest biomass stocks were developed in these
forests (253 t per hectare). Also, the highest rate of tree ferns was encountered in these forests.
Biometry
Results
55
Short Forest
The short forest unit is by far the largest group in terms of documented plots that are
included. These forests were typically found on lower slopes or bottoms of ravines under various
soil moisture and nutrient conditions but also on less exposed upper slopes and ridges. This unit
combined a large variety of stands. For example the stand featuring the largest individual (dbh
144 cm, height 37.4 m) and an above-ground biomass of 197 t per hectare was assigned to this
unit as well as the stand with the highest palm abundance (1 515 individuals per hectare). Low
biomass developed by palms accounted for the low biomass stock of 107.6 t per hectare. Tree
ferns were also found frequently indicating rather moist and cool stand conditions. Mean tree
diameter was highly variable in the short forest, but lower than in the tall forest and higher than in
the dense forest. This was also caused by the large number of thick palms dominating
waterlogged sites. On average, tree height was significantly lower than in the tall forest but in
some cases exceeded 20 m.
Dense Forest
Dense forests are typically found on well drained and nutrient poor upper slopes and ridges
which are both well exposed to intense radiation. No palms and only few tree ferns were
recorded. Stands of this unit are rather uniform in terms of biomass allocation. They are
characterized by a high growth density of 7 900 stems per hectare which is more than twice as
much as in the short forest. These stands were dominated by thin stems (basal area < 25 cm2),
and trees did not reach 20 m in height. The overall mean biomass of 108.1 t per hectare was
comparable to biomass stocks of short forests (107.6 t ha-1).
Open Woodland (Shapumbal & Open Chamizal)
The open woodland unit combines two very distinct vegetation types which shared an open
canopy (expressed by low values for basal area) without palms and tree ferns and only few trees
taller than 5 m. However, shapumbales were only encountered on steep upper slopes on
Quaternary-Pleistocene substrate. Trees reached no more than 10 m in height. Growth density
was low at 600 - 2 300 stems per hectare compared to the chamizal plots and relatively higher in
standing dead wood yielding biomass values between 20 and 50 t per hectare. On the other
hand, chamizales were restricted to the better drained rims of level plateaus of QuaternaryHolocene origin. Vegetation rarely exceeded 5 m in height and reached growth densities of up to
13 500 stems per hectare. Biomass values were low at 7 - 27 t per hectare.
56
Results
Biometry
Shrubland (Dense Chamizal)
The most conspicuous vegetation type found in the study area is classified here as
shrubland. Like the chamizales represented in the open woodland unit, the shrubland occurred
only on level plateaus formed by Quaternary-Holocene sediments but under waterlogged
conditions. This formation was characterized by a dense cover of up to 36 200 short, shrub-like
individuals per hectare of heights less than 10 m. Above-ground biomass was low with an
average of 15.6 t per hectare.
3.2.3.2 Summary
Shrublands were easily identified for their dense cover with thin stems in the shrub layer.
However, shapumbales and chamizales of more open nature were combined in the open
woodland unit based on their similar stand characteristics: open canopy and short trees of height
< 10 m. Biomass was also fairly similar for both types.
Dense forests were found on well drained and nutrient poor ridges of different slopes
throughout the study area. They were characterized by low-statured trees and dominated by a
dense growth of thin individuals. However, biomass was not significantly lower than in short
forests.
Short forests represented the largest cluster combining a wide variety of stands within the
study area. Therefore, due to this high variation among the plots contained, it was difficult to
characterize these stands well. Barranco vegetation, rich in palms and low in stem density, is
included as well as plots from ridges and slopes of different stand height, growth density and
composition of stand characters.
Tall forests featured highest biomass stocks allocated in tall and thick trees but at low stand
density. They are found not only near rivers but also in broad valleys or on terraces of only gentle
inclination. All sites were well drained and provided with phosphorus and nitrogen in the mineral
soil.
The units could be roughly divided into three groups based on their living above-ground
biomass (Fig. 3.30). Shrubland and open woodland units represent the lowest stands which are
also lowest in biomass. Medium values for biomass were calculated for both dense and short
forest types from the upper hill area although the dense version showed lower stand height. Tall
forests were highest in biomass.
Biometry
57
Results
-1
Biometry Units
300
300
250
250
200
200
150
150
100
100
50
50
0
0
0
10
20
30
40
50
60
70
1
2
Shrubland
3
4
5
6
7
0
Dense Forest
2
3
4
5
6
Open Woodland
1
Short Forest
Tall Forest
Fig. 3.30: Relation between soil chemistry and biomass for the respective biometry units.
exchangeable aluminum (Al3+) against the biomass values calculated for the stands of each biometry unit.
Variability for the values given is high throughout.
Fig. 3.30 shows soil parameters most likely to influence plant growth. No toxic effects of
aluminum were indicated since aluminum concentrations did not show an inverse relationship
with biomass. However, the nutrients and phosphorus showed a similar correlation with plant
growth. Phosphorus seems most likely to be the limiting factor since nitrogen supply in all
“forests” is similar yet only the combination of high phosphorus and nitrogen supply seems to
permit the development of high biomass as in tall forests. Vegetation of shrubland and open
woodland must obviously receive their share of phosphorus and nitrogen largely from the organic
layer since supply of these nutrients in the mineral soil is minute.
Statistical analysis of the biometry clusters revealed that soil parameters were not sufficient
to distinguish equally between all units. However, the application of strictly structural parameters
also lead to a reasonable differentiation (cf. One-Way ANOVA results in Appendix 9.2.2.4).
Tab. 3.7: Significant Spearman’s correlation coefficients (values > │0.7│) between soil parameters and
structural parameters assessed in the upper hill area of the Rio Avisado.
Vegetation Parameter
Soil Parameter
2+
CECeff
topsoil
C/N topsoil
Girth Median
-0.75
-0.78
Girth Mean
-0.76
-0.80
Height Median
-0.77
-0.83
Height max
-0.74
-0.82
-1
-0.73
-0.79
2
-0.70
Biomass ha
Basal area 500-1000 cm
2
2
Leaf 225 cm layer1
-0.76
+
pH topsoil
POlsen pool
topsoil
K conc.
subsoil
0.71
0.70
0.72
0.72
0.73
0.72
0.70
0.70
0.74
0.72
0.74
Basal area 1000-2000 cm
+
Mg conc.
subsoil
K pool
subsoil
Thickness
undecomposed
Thickness
organic
horizons
-0.70
-0.74
-0.73
58
Biometry
Results
Tab. 3.8: Comparison of all classified biometric units in respect to tree height, diameter at breast height, basal
area, stem density, biomass and stand characters.
Stand Biometry
Tree
Tree
Height Height
Mean
Max
Biometric Unit
Shrubland
Open Woodland
Dense Forest
Short Forest
Tall Forest
m
Diameter
Mean*
cm
Basal
Area
2
Stand Characters
Amount
Stems**
Amount
Stems**
total
> 10 cm gbh
-1
-1
Stand.
Dead
Tree
Wood Palms Ferns
-1
ha
m ha
Biomass***
ha
t ha
-1
2.3
3.8
4.1
6.2
2.9
5.8
7.3
6.4
24219
5391
1396
1495
15.6
23.1
221
165
0
0
0
0
8.3
11.7
14.7
20.3
12.3
20.7
30.1
25.6
7871
3070
5276
2575
108.1
107.6
160
77
0
140
42
124
20.2
35.7
30.9
30.3
1404
1404
253.3
25
8
39
* = Derived from girth measurements at breast height (1.3 m)
** = Calculated using the angle count method after BITTERLICH (1984)
*** = Calculated using own biomass harvest combined with calculations after OGAWA (1965)
On plot basis, biometric results correlated positively with soil parameters only for phosphorus
which is considered a macro-nutrient and thus may well influence plant growth (cf. Tab. 3.7).
Strong negative correlation effects were found for topsoil C/N and effective cation exchange
capacity (CECeff). The C/N ratio is a measure for microbial activity in the soil and consequently for
mineralization dynamics. Low C/N ratios imply high microbial activity and therefore a rapid
nutrient cycling. In this case, CECeff includes protons (H+) and Al3+ ions which contributed most to
the CECeff in the studied soils. Thus, high values of CECeff also suggest highly acidic conditions
and / or high aluminum saturation which are both considered unfavorable for plant growth.
An overview of biometric parameter means is given in Tab. 3.8 for all units.
Vegetation
Results
59
Tab. 3.9: Assessed parameters and their “Measure of Sampling Adequacy” (MSA) expressing their suitability
for Principal Component Analysis. All parameters listed were selected to represent the individual
plots in a Principal Component Analysis.
The parameters are divided into three categories: Soil properties, biometric properties and structural properties.
Soil
Parameter
Biometry
MSA
Parameter
HCOR_STD
0.898
Leaf 4 cm layer1
COVLAY2
0.886
Number of layers
STEM_HA
0.877
Height mean
HCOR_MEN
0.853
Diameter median
KP_SS
0.835
Height layer1
K pool upper 20 cm
KP_20
0.829
pH topsoil
3+
Al conc. topsoil
PH_TS
0.786
ALC_TS
pH total pit
Ntot conc. subsoil
3+
Fe
3+
conc. topsoil
C/N ratio topsoil
3+
Al conc. subsoil
Clay subsoil
+
K pool subsoil
+
pH subsoil
C/N ratio total pit
-
MSA
Parameter
ALCE_SS
0.904
Height stand. dev.
FEC_SS
0.903
CN_TS
0.889
Cover layer2
-1
Stems ha
ALC_SS
0.872
CLAY_SS
Structure
Code
Al / CEC ratio subsoil
Code
Layer
MSA
BL2CM1
1
0.871
LAYNO
total
0.847
MFVIEL2
2
0.781
0.863
Moss abundance
2
Leaf 50 cm layer3
BL7CM3
3
0.751
DIAM_MED
0.858
Liana abundance
LIVIEL1
1
0.735
HITELAY1
0.855
-
-
-
-
Height max
HCOR_MAX
0.853
-
-
-
-
BAS_AREA
0.838
-
-
-
-
0.784
Basal area
-1
Biomass ha
BMEX_HA
0.837
-
-
-
-
PH_TOT
NC_SS
0.775
0.762
Diameter mean
Height layer2
DIAM_MEN
HITELAY2
0.821
0.819
-
-
-
-
2
Code
PH_SS
0.759
Canopy cover
PLOTCOV
0.800
-
-
-
-
CN_TOT
0.672
Diameter max
DIAM_MAX
0.781
-
-
-
-
-
-
Height layer3
HITELAY3
0.723
-
-
-
-
Overall KMO-Value for 32 Parameters: 0.825 = "respectful "
3.3 Combined Approach
Topography often influences pedogenesis and conditions of stand microclimate which in turn
affect the composition of vegetation. In order to elucidate relationships between soil and
vegetation, which are associated with topographical properties, all recorded parameters of stand
structure were combined with parameters describing the biomass distribution of the stands and
characteristic soil parameters. If such correlations existed, the third null-hypothesis could be
rejected.
For each plot data were available in form of 282 parameters from three categories: soil
texture and chemistry (edaphic condition), biomass distribution (biometric data), and vegetation
structure. The dataset was prepared to contain only mutually independent variables by Principal
Component Analysis. This was required for subsequently submitting the data to a Hierarchical
Cluster Analysis. Tests were performed to identify highly suitable parameters for Principal
Component Analysis. All variables with a computed “Measure of Sampling Adequacy” (MSA) of
less than 0.7 are considered “mediocre” on the KAISER scale (cf. Tab. 2.10 in chapter 2.3.2.2) and
were excluded from further analysis. The initial dataset was reduced to 32 parameters listed in
Tab. 3.9. All three categories: edaphic conditions, biometric characteristics and structure
parameters were still represented. The achieved KAISER-MEYER-OLKIN (KMO) value of 0.825 for
the factor model rated it “respectful” according to KAISER & RICE in BACKHAUS et al. (1996). Four
factors were extracted using the KAISER criterion and were then VARIMAX rotated (see regression
60
Vegetation
Results
Tab. 3.10: Eigenvalues of the four factors extracted by Principal Component Analysis.
The four mutually independent factors together explain over 79 % of the variance within the dataset. Factors
from Principal Component Analysis were extracted to be mutually independent, iteratively explaining a maximum
of the variance within a dataset in a multidimensional space. Since variables were Z-standardized to a mean of 0
and a standard deviation of 1 before analysis each variable therefore has a variance of 1. Consequently a factor
with an eigenvalue of < 1 is not significant since it describes less variance within the dataset than there is within
each variable itself. For this reason only factors with an eigenvalue of > 1 are considered. Rotation of the factors
in space (with their relative position to each other maintained) apportions the explained variance more evenly to
the factors.
Initial Eigenvalues
FACTOR
Variance
explained
Total
Rotated Values
Cumulative
Total
Variance
explained
%
1
2
3
4
18.6
3.0
2.4
1.3
58.2
9.5
7.6
4.0
Cumulative
%
58.2
67.7
75.2
79.2
8.3
7.1
6.8
3.2
26.0
22.2
21.1
9.9
26.0
48.2
69.4
79.2
values for factor scores in Tab. 9.10 in Appendix 9.2.2.1). The resulting four factors together
explained over 79 % of the total variance in the data set (Tab. 3.10).
Examination of factor loadings (Tab. 9.11 in Appendix 9.2.2.2) suggested that only factor 3
clearly represented one certain category of parameters. Only soil parameters loaded high on this
factor. Biometry parameters were of minor importance to the composition of factor 4.
As expected for factors of a Principal Component Analysis the first factor loaded high on only
few parameters, but from all categories. The parameters a) potassium in the subsoil, b) tree
diameter and c) layering of the stands appeared suitable to express the majority of the variance
within the dataset. Structural parameters describing layering, epiphyte situation and canopy
quality combined with biomass and growth density characterized factor 2. Factor 3 was loaded
exclusively with soil characteristics. Epiphytes and soil pH influenced factor 4 the most.
Regressional scores of all four factors which had been extracted by Principal Component
Analysis were subsequently submitted to a Hierarchical Component Analysis. Outliers were first
identified by Hierarchical Component Analysis using the Single Linkage method (Fig. 9.4 in
Appendix 9.2.2.2). Three plots were excluded by this criterion (P11, 12, and 41). Data for the
600
Fig. 3.31: Combined analysis: Number of
clusters plotted against the employed
distance measure: squared Euclidean
distance (BACKHAUS et al. 1996).
500
400
Faint Elbow
The initial cluster solution is marked by an
“elbow” in the line where the distance
between each additional cluster does not
increase significantly any more.
300
200
100
0
5
10
15
20
25
30
Number of Clusters
35
40
45
Vegetation
61
Results
Ward Method with new Clusters split off
Mostly Sloped
Terrain
Cluster 1
Cluster 2
Cluster 3
Dry
Chamizal
Wet Chamizal
Dry Rainforest
Cluster 4
Cluster 5
Bottom & Lower
Parts
Cluster 6
Drained
Cluster 7
P04
P16
P08
P43
P14
P06
P42
ó ó ÷ò
òò
òò
òòò
ò
òòò
ò Short
ó ó òù ó ÷
òò
òò
òò
ò
÷ò
øò òù ó ÷ò
òò
øò ôò ÷ò øò ôò ÷ò
P01
P44
P03
P41
P42
P42
P13
P09
P05
ó ó ó ó ó ó ó ó ÷ò
òò
òò
òò
ò
Wet
øò ó ó ó òù ó
òò
òò
òòò
ò
òòò
òò
Tall
ò
ø
òò
òò
òò
òò
ò÷
òò
òò
òò
òò
ò
òò
øò ù ÷ò
øò úò òù
òò
øò ôò ÷ò òò
òò ûò ÷ò
P40
P07
P15
P10
P42
P21
P20
P12
P11
P42
P39
P38
P34
P02
P37
P17
P45
ó ó ó ó ó ó ó ó ó ó ó ó ó ó ó ó ÷ò
òò
òòò
ò
Upper & Top
òò
ò
Parts
øò ó ó ó ó ó ó ó ó ó òù
òò
òò
òò
òò
Upper
Top
òò
Parts
Parts
òò
òò
òòò
ò
òøò ó ó ó ù ó ó ó óø óó ò÷ù ó ó ÷
òò
òTall
Short òò
òò
ò
ò
òò
òò
òò
òò
òò
òò
òò
òò
òû ó
ø
ó
ù
ó
÷
ò
òò
ò
ù ÷ò
øò òù ó ÷ò
øò òù ÷ò
òò
÷
ò
ø
ô
÷
ø
ô
÷
û
÷
û
÷
ø
ò ò ò ò ò ò ò ò
ò ò ò ò
ò ôò ÷ò
P18
P36
P35
P42
P31
P22
P29
P28
P27
P25
P26
P24
P23
P42
P19
P30
P33
P32
Plot
ø
ò ó ó ó ó ó ó ó ó ó ó ó
ò
ò
ò
ò
ò
ò
Plat eau
ò
ò
ò
ò
ò
ò
ò
ò
ò
ò
ò
ò
ò
Drained
Wet
ò
ò
ò
ò
ò
ò
ø
ó ó ò
ù ó ó ÷
ò
ò
ò
ò
ò
ò
ò
ò
ò
ò
ò
ò
ò
ò
ò
ò
ò
ò
ò
ò
ò
ò
ò
ò
ò
ò
ò
ò
ò
ò
ò
ò
ò
ò
ò
ò
ø
ò
ò
ò
ø
ú
ô
÷
ø
ú
ú
ø
ò ò ò ò
ò ò ò ô
ò ú
ò ú
ò ú
ò ú
ò ÷
ò
ò ô
ò
P42
Level
Terrain
Cluster 8
Shapum- Impover. Palm Rich Premont. Low Canopy
Rainforest Rainforest Rainforest
Rainforest
bal
Fig. 3.32: Dendrogram generated by Hierarchical Cluster Analysis using Ward’s Method.
Three outlier plots were re-integrated into the clusters based on their similar properties.
The outlier plots P11, 12, and 41 of the initial statistical analysis are encircled.
Distinction of clusters was confirmed by statistical analysis (cf. ANOVA-Analysis) on the basis of topographic
and moisture situation of the sites, and stand height.
remaining 42 plots were clustered using Ward’s method. The “elbow” criterion was used to decide
on the number of final clusters. Eight clusters were suggested (
Fig. 3.31), however, after further analysis of the cluster dendrogram only six clusters were
chosen (cf. Fig. 9.5 in Appendix 9.2.2.2). A separation into eight clusters would have resulted in
plot P40 representing a single cluster. This plot had previously not been identified as outlier and
therefore interpretation of this cluster solution was limited. The seventh cluster was essentially
composed of premontane rainforest plots but merged with the neighboring cluster on a similar
level as plot P40. Eventually, this cluster was split off from the super-ordinate arrangement
post-hoc (Fig. 3.32).
Investigation of common structural properties within each group was conducted by
calculating t-values (BACKHAUS et al. 1996) for each parameter (see Tab. 9.12 - Tab. 9.19 in
Appendix 9.2.2.3 for tabled t-values). Three outlier plots were post-hoc assigned to the cluster to
which they showed greatest similarity. Fig. 3.32 shows the cluster combination of the modified
dendrogram where re-integrated outliers are marked by circles. Clusters 1 and 3 as well as
clusters 5 and 6 had been separated out of one initial cluster based on different morphological
62
Vegetation
Results
Tab. 3.11: Terrain inclination, elevation above sea level, and slope exposition as used to characterize the
topographical situation for the classified units.
Units are arranged by their position within the hill area from top to bottom. Standard deviations are given in
parentheses.
Vegetation Unit
Inclination
Elevation
°
Wet Chamizal
Dry Chamizal
Impoverished Rainforest
Shapumbal
Dry Rainforest
Low Canopy Rainforest
Palm Rich Rainforest
Premontane Rainforest
0
2
17
29
20
17
10
13
Exposition
m
(1)
(2)
(16)
(9)
(13)
(8)
(2)
(9)
985
985
961
977
1063
972
971
1013
°
(16)
(16)
(51)
(8)
(97)
(17)
(15)
(12)
9
210
98
193
253
201
138
156
(28)
(113)
(89)
(134)
(76)
(88)
(72)
(57)
properties of the stands (e. g. stand height). All analytical steps can be reproduced with
dendrograms in Fig. 9.4 and Fig. 9.5 in Appendix 9.2.2.2.
The cluster combinations could be explained by the topographic position of the combined
plots. Two groups could be found on level terrain (inclination < 5°) which could be identified as
plateau position from field observations. This branch splits into the “Dry Chamizal” (Cluster 1) on
drained sites and the “Wet Chamizal” (Cluster 2) on sites frequently affected by stagnant water.
For groups on sloped terrain (inclination > 5°) it was again possible to ascribe them to specific
topographic positions in the next step. Plots from upper slopes were combined in the “Dry
Rainforest” (Cluster 3) whereas plots encountered on the top parts of slopes were further divided
into short stands of maximum tree heights < 10 m which corresponded to “Shapumbales”
(Cluster 4) and taller stands with tree heights > 10 m which represented the “Impoverished
Rainforest” (Cluster 5). Plots found on lower slopes and valley bottoms were also distinguished
further. Plots on wet sites characterized the “Palm Rich Rainforest” (Cluster 6). Drained sites
featured tall stands with trees > 30 m in height and were combined in the “Premontane
Rainforest” (Cluster 7) whereas the cluster containing plots of lower stand height (< 30 m) was
termed “Low Canopy Rainforest” (Cluster 8).
Vegetation
Results
63
3.3.2 Results of Combined Analysis
The rationale for this classification approach was to identify possible topographic factors
controlling the development of different forest types. Tab. 3.11 lists mean values of the recorded
topographical parameters a) slope of terrain (inclination), b) elevation above sea level and c)
exposition for the units classified by this approach. Little significant information could be derived
from this analysis. Both chamizal types are found on least inclined sites, whereas slopes of
shapumbal sites are steepest. Slopes of palm rich rainforest sites are most consistent and slopes
of impoverished rainforest sites most variable since these include steep to level sites. It may also
be noted that premontane rainforests are not necessarily restricted to level terrain.
Results of the combined analysis will be presented separately for soil, biomass and structural
characteristics of the units which were chosen.
3.3.2.1 Soil
Within each classified unit, means and standard deviations were calculated for all plots
contained. Characteristic soil properties were compared between units.
In terms of soil texture the palm rich rainforest featured the highest proportion of clay in the
topsoil and highest silt and lowest sand in the subsoil (Fig. 3.33 and Fig. 3.34). Relatively high
subsoil clay contents were found in impoverished rainforests and in shapumbales. Subsoils in the
premontane rainforests contained up to 50 % sand. Dry chamizales contained the highest
proportion of sand in the topsoil and were almost exclusively made up of sand in the subsoil.
Topsoil texture composition for wet chamizales was average although the high organic matter
content may have disguised higher sand content as discussed in chapter 3.1.2. Subsoils of wet
chamizales were also rich in sand.
Soil pH was in the aluminum buffer range (between 3.5 and 4.5) for most topsoils and
subsoils (Fig. 3.35). The only exceptions were wet chamizales and the topsoil of dry chamizales.
Combination Clusters
Sand
Silt
Clay
Proportion [%]
Proportion [%]
80
60
40
100
100
80
60
40
20
20
0
0
Wet
Dry
Chamizal Chamizal
Shapum- Impoverished Dry Low Canopy Palm Rich Premont.
bal
RF
RF
RF
RF
RF
Fig. 3.33: Topsoil texture composition of the
fractions sand, silt, and clay of the
classified units of the study area.
Wet
Dry
Chamizal Chamizal
bal
RF
RF
RF
RF
RF
Fig. 3.34: Subsoil texture composition of the
fractions sand, silt, and clay of the
classified units of the study area.
64
Vegetation
Results
5.0
50
topsoil
subsoil
topsoil
subsoil
4.5
40
CN ratio
pH value
4.0
3.5
30
20
3.0
10
2.5
0.0
0
Wet
Dry
Chamizal Chamizal
bal
RF
RF
RF
RF
RF
Fig. 3.35: The pH values in the classified units.
Wet
Dry
Chamizal Chamizal
bal
RF
RF
RF
RF
RF
Fig. 3.36: Distribution of C/N ratio in the classified
units of the study area.
For chamizales a large discrepancy between lower topsoil and higher subsoil pH was observed.
However, the pH of dry chamizales was always higher than of wet chamizales which is a clear
effect of high concentrations of organic acids involved.
The C/N ratios showed high and consistent values of approx. 30 for the topsoils of both
chamizal types (Fig. 3.36). It is remarkable that C/N ratios for wet chamizales were even wider at
approx. 40 in the subsoil, whereas subsoils of the dry types featured narrower but highly variable
C/N ratios of approx. 20. Also, C/N ratios for topsoil and subsoil of shapumbal soils differed
significantly as it was observed previously.
Carbon (Ctot) concentrations were always significantly higher in topsoil than in subsoil, except
on lower slopes (Fig. 3.37). Variation was highest for Ctot in topsoils of the premontane rainforests
and lowest in the low canopy rainforest. Highest Ctot concentrations in the subsoil were found for
the dry rainforests where podzols were present and in the topsoil for wet chamizales. Subsoil
carbon pools were highest in both dry and low canopy rainforests which might not only indicate
6
120
topsoil
subsoil
topsoil
subsoil
C tot content [t ha ]
4
-1
Ctot content [%]
100
2
80
60
40
20
0
0
Wet
Dry
Chamizal Chamizal
bal
RF
RF
RF
RF
RF
Fig. 3.37: Distribution of Ctot concentration in the
respective classified units.
Wet
Dry
Chamizal Chamizal
bal
RF
RF
RF
RF
RF
Fig. 3.38: Distribution of Ctot pools
respective classified units.
in
the
Vegetation
12
65
Results
80
10
-1
POlsen content [t ha ]
60
8
6
4
40
20
Al
3+
content [cmolc kg
-1
soil]
topsoil
subsoil
2
0
0
Wet
Dry
Chamizal Chamizal
Wet
Dry
Chamizal Chamizal
bal
RF
RF
RF
RF
RF
Fig. 3.39: Distribution of
concentration.
exchangeable
Al3+
bal
RF
RF
RF
RF
RF
Fig. 3.40: Distribution of POlsen pools in the topsoil.
the presence of podzols but also that buried organic horizons existed after mass transport
(landslides). Highest topsoil C-pools were calculated for wet chamizales with profound organic
horizons (Fig. 3.38).
Concentrations of exchangeable aluminum (Al3+) were lowest in the heath forests (Fig. 3.39).
Highest concentration in the topsoils were calculated for shapumbales. A slight trend for rising
Al3+ concentrations in the topsoil with rising position on the slope can be observed. Variation for
Al3+ concentration is high throughout all non-chamizal units, especially in the premontane
rainforest. Therefore, no predictions can be made for these units in terms of aluminum status.
Pools of plant-available phosphorus are shown for topsoils in Fig. 3.40. Again, variation is
high. Classified units can be divided into three different groups: Most phosphorus is found in
premontane rainforest and palm rich rainforest soils. Some phosphorus is available in soils on the
slopes under low canopy, dry and impoverished rainforest. Lowest pools were calculated for
2.0
30
1.5
20
1.0
10
0.5
0.0
0
Wet
Dry
Chamizal Chamizal
Shapum- Impoverished Dry
bal
RF
RF
Low Canopy Palm Rich Premont.
RF
RF
RF
Fig. 3.41: Topsoil contribution of the exchangeable portion of bases (K+, Ca2+, and
capacity (CECeff).
Concentration [cmol c kg-1 soil]
CECeff
40
Concentration [cmolc kg-1 soil]
2.5
50
3.0
2+
Ca
Mg2+
K+
2.5
40
2.0
30
1.5
20
1.0
50
3.0
10
0.5
0.0
0
Wet
Dry
Chamizal Chamizal
Shapum- Impoverished Dry
bal
RF
RF
Low Canopy Palm Rich Premont.
RF
RF
RF
Fig. 3.42: Subsoil contribution of the exchangeable portion of bases (K+, Ca2+, and
capacity (CECeff).
66
Vegetation
Results
Tab. 3.12: Comparison of all classified units by the combined approach in respect to thickness of topsoil, pH,
and nitrogen, phosphorus and potassium pools and concentrations.
Ntot
pH
Thickness
pool
t ha
topsoil
topsoil subsoil
K2O*
concentration
-1
upper
subsoil
20 cm
pool
%
total
kg ha
topsoil subsoil
concentration
-1
-1
upper
subsoil
20 cm
P2O5**
total
pool
-1
conc.
cmolc kg soil
kg ha
topsoil subsoil
upper
20 cm
topsoil
ppm
Wet Chamizal
19
2.7
3.3
1.8
0.0
28
0.10
0.00
1
1
Dry Chamizal
12
3.3
4.1
0.9
0.1
0.9
0.10
0.00
21
0
21
0.11
0.00
0
0
Shapumbal
11
3.7
3.8
1.9
1.7
3.0
0.15
0.03
168
412
494
0.23
0.16
3
3
14
3.8
4.0
2.3
2.7
4.7
0.15
0.05
320
578
835
0.43
0.25
19
8
Dry Rainforest
14
4.1
4.2
3.4
4.7
7.1
0.18
0.07
130
292
386
0.17
0.12
14
10
12
3.7
3.9
1.9
3.9
5.1
0.11
0.06
141
447
536
0.19
0.18
12
8
17
4.0
4.0
3.5
2.9
6.3
0.19
0.04
276
710
953
0.35
0.29
33
10
16
3.9
4.0
3.0
3.5
6.1
0.19
0.06
191
402
587
0.27
0.19
46
28
* = NH4NO3-Extraction
1.8
0.16
0.01
28
0
** = NaHCO 3- Extraction
shapumbales and wet chamizales. Dry chamizales are virtually devoid of any plant-available
phosphorus.
In terms of exchangeable micro-nutrients the palm rich rainforest soils show the highest
concentrations of base cations both in topsoils and subsoil (Fig. 3.41 and Fig. 3.42). However, in
topsoils of palm rich rainforests an unfavorable ratio between calcium and magnesium was found,
possibly leading to magnesium shortage due to an antagonistic process with calcium. The overall
micro-nutrient situation in topsoils and subsoils seems most favorable in premontane rainforest
soils, in spite of lower base cation concentrations. Topsoil content of base cations is surprisingly
high under the impoverished rainforest. Subsoils of dry chamizales are devoid of any
exchangeable base cations which are probably rapidly leached under conditions of good
drainage. This is in contrast to wet chamizales. Potassium is found in all soils, with the highest
values occurring under the impoverished rainforest.
Tab. 3.12 compares all units from the combined analysis based on the soil nutrient status.
Vegetation
500
25000
20000
10000
Woody Stems [ha-1]
Woody Individuals [ha-1]
400
300
200
100
0
30000
Tree Ferns
Standing Dead Wood
Palms
67
Results
7500
5000
2500
ill
e
o
y
l
e
0
l
a
g
e
iza
lop
r HDry Low
mb Rich am
anc
Wet
ShapumImpoverished
Premont.
RidCanopyap uPalm
VallDry
p pe
er S
Barr
Ch
Chamizal Chamizal
bal Low
RF U
RF
RF Sh
RF
RF
Dry
Fig. 3.43: Density of non-tree woody individuals
(tree ferns, palms) and standing dead
wood for each classified unit.
ey
co
pe
pe
al
al
ge
iz
mb Richham
an
Slo Dry Low
Slo
RidCanopyapuPalm
Wet
VallDry
ShapumImpoverished
Premont.
rr
C
Ba
per
wer
h
Dry
Chamizal Chamizal
bal Lo
RFUp
RF
RF S
RF
RF
Fig. 3.44: Growth density means (expressed in
stems per hectare) plotted for each
classified unit with standard deviations
3.3.2.2 Biometry
For each biometric unit derived from combined analysis the means and standard deviations
were calculated for all plots. In this chapter the values for characteristic biometrical properties are
compared between these units.
Examination of the stand characteristics within the units shows that the number of tree ferns
decreases steadily downward on the slopes from the impoverished rainforest to the palm rich
rainforest (Fig. 3.43). In relation to total stem density, however, tree ferns are most abundant in
the premontane rainforest. No tree ferns were found in vegetation types with open canopy such
as the chamizales and the shapumbal. Palms abound in palm rich rainforests and low canopy
rainforests. They can be found in the undergrowth also in impoverished rainforests. No palms
were recorded for shapumbales and chamizales although some individuals of Mauritiella peruana
were observed protruding the wet chamizal vegetation. Most stems of standing dead wood were
found in wet chamizales. However, due to their thin nature they did not contribute much to total
biomass. The least standing dead wood occurred in premontane rainforest but overall stem
25
140
20
100
Tree Height [m]
120
80
60
15
10
40
5
20
0
ey
co
pe
pe
ge
bal
al
iz
m
m
SloDry Low
ran
RidCanopyapuPalm
Slo
VallDry
Wet
ShapumImpoverished
Rich
Premont.
Cha
Bar
per
h
wer
Dry
Chamizal Chamizal
bal Lo
RFUp
RF
RF S
RF
RF
Fig. 3.45: Girth at breast height means plotted for
each classified unit with standard
deviations.
0
ey
co
pe
pe
ge
al
al
iz
an
mb Richham
SloDry Low
Wet
VallDry
ShapumImpoverished
Premont.
RidCanopyap uPalm
rr
r Slo
Ba
C
per
we
h
Chamizal Chamizal
bal Lo
RFUp
RF
RF S
RF
RF
Dry
Fig. 3.46: Tree height means plotted for each
classified unit with standard deviations.
68
Vegetation
Results
30
20
10
0
ey
co
pe
pe
300
Biomass above Ground [t ha-1]
Basal Area [m2 ha-1]
40
ge
al
al
iz
mb Richham
SloDry Low
RidCanopyapuPalm
Wet
VallDry
ShapumImpoverished
Premont.
rran
r Slo
Ba
C
per
we
h
Chamizal Chamizal
bal Lo
RFUp
RF
RF S
RF
RF
Dry
Fig. 3.47: Basal area means plotted for each
classified unit with standard deviations.
250
200
150
100
50
0
ey
co
pe
pe
ge
al
al
iz
mb Richham
Slo
SloDry Low
RidCanopyap uPalm
Wet
VallDry
ShapumImpoverished
Premont.
rran
Ba
C
per
wer
h
Chamizal Chamizal
bal Lo
RFUp
RF
RF S
RF
RF
Dry
Fig. 3.48: Standing above ground biomass means
plotted for each classified unit with
standard deviations.
density was also low (Fig. 3.44). In relative terms, the highest rates of standing dead wood were
calculated for shapumbales.
Lowest growth density was found in palm rich rainforests and shapumbales with less than
1 500 stems per hectare. In premontane rainforest an average of approx. 2 000 stems per hectare
were recorded. Significantly denser were dry and impoverished rainforests with up to 6 000 stems
per hectare. Highest rates were found for chamizales with an average density of approx. 9 000
and 18 000 stems per hectare for dry and wet chamizales respectively.
A tendency of stem thickness to decrease with the topographic position upward on the slope
could be observed (Fig. 3.45). Exceptions were the impoverished and lower canopy rainforests
where thick palm trees raised the mean diameter of the plots. Shapumbales show very little
variation in stem thickness suggesting that the plots are stocked with similar individuals.
Tree height tends to decrease steadily on slopes from bottom to top (Fig. 3.46). Only
impoverished rainforests on the ridges display trees of heights similar to low canopy rainforests
on lower slopes. This might be caused by these regions being less frequently affected by
landslides, and taller individuals are more likely to survive recurring fires.
Based on values for basal area the vegetation units could only be divided into two groups: a)
closed canopy formations with high basal area of approx. 30 m2 per hectare and b) open canopy
formations like shapumbal and both chamizales with basal area less than 10 m 2 per hectare
(Fig. 3.47).
The biomass values suggest a grouping into a) high, b) moderate and c) low biomass groups
(Fig. 3.48). The premontane rainforest exceeded all other units significantly at 238.7 t per
hectare. Lowest in above-ground biomass were the open canopy formations shapumbal with
36.4, dry chamizal with 25.1 and wet chamizal with 13.9 t per hectare. All other units developed a
total biomass of slightly over 100 t per hectare. Palm rich rainforests showed a very high standard
Vegetation
Results
69
Tab. 3.13: Comparison of all classified units by the combined approach in respect to tree height, diameter at
breast height, basal area, stem density, biomass and stand characters.
Stand Biometry
Tree
Tree
Height Height
Mean
Max
m
Wet Chamizal
Dry Chamizal
Shapumbal
Dry Rainforest
Diameter
Mean*
cm
Basal
Area
2
Stand Characters
Amount
Stems**
total
-1
> 10 cm gbh
ha
m ha
Amount
Stems**
-1
Biomass***
Stand.
Dead
Tree
Wood Palms Ferns
t ha-1
ha-1
2.4
4.2
3.6
6.4
17805
1284
13.9
266
0
0
4.4
4.9
10.6
7.7
7.6
18.8
6.6
7.4
16.3
9.2
6.8
30.5
8966
1337
5541
2262
1241
4348
25.1
36.4
126.4
39
127
41
0
0
53
0
0
309
9.4
11.9
15.2
16.2
20.4
23.5
13.8
19.8
33.4
27.3
28.0
23.6
5985
3115
1175
4169
2588
1175
107.7
118.2
103.5
132
93
93
16
60
451
102
45
0
18.7
36.4
28.5
30.1
2081
1835
238.7
27
4
39
* = Derived from girth measurements at breast height (1.3 m)
** = Calculated using the angle count method after BITTERLICH (1984)
*** = Calculated using own biomass harvest combined with calculations after OGAW A (1965)
deviation in biomass which was caused by one plot that had been assigned to this cluster for
reasons of similar soil moisture conditions but had an unusually high biomass of 255 t per
hectare. Otherwise biomass stock in this unit would be far below 100 t per hectare.
Tab. 3.13 compares the mean values of biometric stand parameters for all units from the
combined classification.
Vegetation
Results
Proportion of Foliage in Lower Tree Layer [%]
80
2
0 - 4 cm
4 - 50 cm2
50 - 225 cm2
2
> 225 cm
60
40
20
0
Wet
Dry
Chamizal Chamizal
bal
RF
RF
RF
RF
RF
Fig. 3.49: Proportional leaf size distribution in the
lower tree layer (1).
Proportion of Foliage in Medium Tree Layer [%]
70
80
2
0 - 4 cm
4 - 50 cm2
50 - 225 cm2
2
> 225cm
60
40
20
0
ey
co
pe
ill
ge
bal
al
H
iz
m
ran
Slo
VallDry
RidCanopyapuPalm
Wet
ShapumImpoverished
Rich
Premont.
am
per Dry Low
Bar
Ch
wer
Up
h
Dry
Chamizal Chamizal
bal L o
RF
RF
RF S
RF
RF
Fig. 3.50: Proportional leaf size distribution in the
medium tree layer (2).
Four leaf size classes were recorded in each layer.
Shown are mean estimated proportions of these leaf size classes for each classified unit.
3.3.2.3 Structure
In this chapter values for characteristic properties of stand structure are compared between
units. For each unit derived from combined analysis the means and standard deviations were
calculated over all plots contained.
Leaf sizes may reflect stand climatic conditions (RICHARDS 1983). Therefore, special attention
was paid to leaf sizes recorded in the field. Underestimation of leaf size might be caused when
measurements occur during the dry season and foliage from deciduous trees is underrepresented. Four leaf size classes were separated in each canopy layer. Fig. 3.49 shows the
distribution of leaf sizes of the lower tree layer within the vegetation units. Thus, totals of the
proportions displayed may exceed 100 %. Remarkable is the presence of very small leaves
(0 - 4 cm 2) in this layer only in open canopy formations. For all other units the contribution of these
small leaves is marginal in this layer. Medium sized leaves (4 - 50 cm2) dominate this layer both in
open canopy formations and on slopes. In impoverished and in palm rich rainforests all leaf sizes
larger than 4 cm 2 are represented equally. Highest proportions of large leaves (> 225 cm 2) were
encountered in impoverished and in palm rich rainforests indicating either the presence of large
leaved Melastomataceae or palms. Leaves of 50 - 225 cm2 dominate in the lower tree layer of
premontane rainforests.
In most shapumbal and chamizal stands the medium tree layer (2) constituted the actual top
layer (Fig. 3.50). Whereas the proportion of smallest leaves increased slightly for the dry
chamizales, medium sized leaves (4 - 50 cm 2) made up almost the entire foliage in the wet
chamizales indicating permanently good water availability even under high insolation.
Shapumbales showed highest proportions of small leaves in the medium layer, suggesting high
water stress on these sites. The largest leaf class of more than 225 cm 2 (i. e. palms) was well
represented in palm rich rainforests.
Vegetation
Top Layer
Medium Layer
Lower Layer
25
Layer Height [m]
Total Canopy Cover
Medium Tree Layer
Canopy Cover per Layer [%]
30
20
15
10
100
35
71
Results
80
Low Tree / Shrub Layer
60
40
20
5
0
0
Wet
Dry
Chamizal Chamizal
bal
RF
RF
RF
RF
RF
Fig. 3.51: Cumulative canopy height chart.
A maximum of three canopy layers was
distinguished at each plot. Shown are mean
estimated heights of these layers for each
classified unit.
ey
co
pe
ill
ge
al
al
ll
H
iz
an
mb Rich am
Slo
RidCanopyapuPalm
Wet
Va Dry
ShapumImpoverished
Premont.
per Dry Low
Barr
Ch
wer
h
Up
Dry
Chamizal Chamizal
bal Lo
RF
RF
RF S
RF
RF
Fig. 3.52: Canopy cover by each individual layer.
Layer (1) includes all woody individuals on
the ground up to the top of this relative layer.
Layer (2) includes all trees between layer (1)
and the top canopy layer. Total canopy cover
was measured > 1.3 m.
Canopy layering may allow conclusions to be made about light availability in a forest stand.
Heights of all identified canopy layers were recorded and the vertical layer structure was
compared for all units (Fig. 3.51). For open canopy formations only two canopy layers were
identified. In chamizales the height of the lower layer is similar, yet the upper layer is slightly
higher in dry chamizales. In shapumbales the distinction between a fairly high lower layer to the
short upper layer was difficult. Layer proportions on the slopes are similar with a distinct upper
layer clearly separated in height from the medium layer. Conditions are similar in the
impoverished rainforest yet the height I greater overall. The layering structure for palm rich and
impoverished rainforests was comparable because both lower and medium layers were relatively
tall.
The contribution of the individual layers to the total canopy cover expresses the horizontal
layer structure and may also indicate the gradient for available light within the stand. In Fig. 3.52
the estimated cover of the lower layer (including shrubs) and of the medium layer is compared to
recorded total canopy cover. Because total canopy cover was assessed above 1.3 m (this
distinction was not made for the shrub layer) the cover of the shrub layer may exceed the total
canopy cover. Total canopy cover was high for all units of the hill area. Within this group,
proportional cover by the lower tree layer was highest in palm rich rainforest which abounded in
small palms and was lowest in the low canopy rainforest which may be an indication for poor light
availability at the bottom of these stands. Lowest canopy cover was recorded in wet chamizales
and shapumbales whereas dry chamizales showed medium canopy cover values. The shrub
layer was dense in wet chamizales whereas cover of all layers was low in the shapumbal
accounting for the thick and impenetrable layer of ferns strongly suppressing the re-growth of
other species. Total canopy cover in dry chamizales reached approx. 50 % and was significantly
higher than in the wet chamizales (10 %).
72
Results
Vegetation
3.3.3 Description
The following chapter briefly summarizes the most conspicuous characteristics of the
classified soil units and assesses their implications for plant growth. The prevalent topographic
situation where plots of each unit was found is mentioned in parentheses.
3.3.3.1 Vegetation Units
Wet Chamizal (Plateau)
Wet chamizales occur often adjacent to dry chamizales on the same plateaus but cover
waterlogged sites. Soils were composed of white sands and were very acidic, especially in the
organic topsoil with widest C/N ratios in the subsoil. Drainage was always obstructed by an
impenetrable layer of sediments (hard pan). Carbon pools were highest in the topsoil. Aluminum
and nutrient concentrations were very low. The upper shrub layer cover exceeded the cover by
trees. Trees were short (< 5 m) and bore almost exclusively leaves of 4 - 50 cm2. Growth density
was extremely high with approx. 17 800 stems per hectare. The stand biomass was lowest at an
Dry Chamizal (Drained Plateau)
Dry chamizales were found exclusively on the drained portions on rims of plateaus formed by
Quaternary-Holocene sediments. Soils were mainly composed of sand and very acidic especially
in the topsoil. C/N ratios were approximately 30. Aluminum concentrations as well as pools for all
nutrients were very low in the mineral soils. Although medium sized leaves (4 - 50 cm2) dominated
the upper layer, dry chamizales showed the same amount of small leaves < 4 cm 2 as the
shapumbal vegetation suggesting occasional water stress. Overall stem density was variable but
high with a remarkably small dead wood proportion. Trees did not reach 10 m and biomass
averaged 25.1 t per hectare.
Shapumbal (Steep Upper Slopes)
Shapumbales were confined to steep upper slopes on Quaternary-Pleistocene sediments.
These sediments are highly susceptible to landslides. Shapumbales are open stands of few
stunted and small leaved trees with a thick and impenetrable understorey of the ferns Sticherus
remotus and Gleichenella pectinata. Burnt snags and considerable amounts of charcoal in the
soil were found at some sites. Common soil characteristics were a thick organic litter layer formed
by the ferns, fairly high contents of clay and aluminum. Soils in general were highly variable.
Nutrients such as phosphorus were very low and C/N ratios varied significantly from topsoil to
subsoil. Neither palms nor tree ferns were found. Canopy cover in both recorded tree layers was
low and the upper layer is almost exclusively made up of leaves < 4 cm2. Stem density is low with
slightly over 1 300 stems per hectare but the proportion of standing dead wood is high. The
Vegetation
Results
73
conspicuously twisted trees reach heights between 5 and 10 m. Biomass was low with an
Impoverished Rainforest (Ridges)
Impoverished rainforest developed over all geological formations in the study area. They
were, however, found both on steep slopes close to narrow crests as well as on wide and level
ridge tops. Subsoils are relatively often rich in clay and high in exchangeable potassium. Except
for the smallest leaves (0 - 4 cm 2) all other sizes were equally represented in the lowest layer and
the boundary between medium and top layer is not pronounced. The highest number of tree ferns
was found in these forests. Woody individuals were slightly taller and thicker than in the dry and
low canopy rainforests. This may be caused either by reduced susceptibility of these sites to
landslides and thus longer growth periods or by the fact that these trees are more likely to survive
recurrent fires. Biomass was calculated to be 126.4 t per hectare.
Dry Rainforest (Upper Slopes)
Dry rainforests were mostly found at the slopes of the Cordillera Cahuapanas on late
Jurassic substrate. Soil parameters did not allow any significant distinctions to the low canopy
rainforest. Only pH values and carbon pools in the subsoil tended to be slightly higher. The fact
that fewer palms but more tree ferns were recorded might indicate a cooler and possibly even
moister micro-climate which is likely especially at the slopes of the Cordillera Cahuapanas.
Higher rates of precipitation were found there by METTE (2001). Stand height is even shorter than
in low canopy rainforest. Stems are thinner and more abundant. Biomass at approx. 107 t per
hectare is comparable to values from the low canopy rainforest.
Low Canopy Rainforest (Lower Slopes)
The largest portion of the upper hill region, especially the very finely layered and unstable
Quaternary-Pleistocene section is made up of sloped terrain. Landslides are frequent in this area
therefore vegetation must be adapted to highly dynamic edaphic situations. Stand conditions on
lower slopes are slightly more favorable since this part is better sheltered from adverse climatic
conditions, moister in stand climate and better supplied with nutrients due to accumulation from
above. Soils are highly variable under these conditions. Furthermore the low canopy rainforests
feature soils from different geological substrate of Tertiary and late Jurassic material. Thus, no
distinctions for characteristic soil properties in this unit can be made. Stands were slightly shorter
than in palm rich rainforests but with clearer layer structure, less palms and more tree ferns
present. Calculated biomass was 118 t per hectare which was slightly higher than in palm rich
rainforests.
74
Results
Vegetation
Palm Rich Rainforest (Barrancos / Ravines)
Barrancos, where palm rich rainforests were found primarily, are rather steep ravines or
narrow valleys primarily found in the dissected landscape on Quaternary-Pleistocene sediments.
Palm rich rainforests were characterized by a large number of palms which indicated stagnant
and wet soils. Slope inclination ranged from almost level to greater than 10°. Due to a low overall
stem density, slender trees and relatively unproductive palms biomass is rather low for these
stands. One plot, structurally belonging to the premontane type, was assigned to the palm rich
rainforests for its soil conditions and thus disguised the lower biomass value of this unit. Medium
and top tree layers were not well separated and lianas were most frequent in the lowest layer.
Clay content, phosphorus and base cation concentrations in the topsoil were high and were likely
due to accumulation from upslope. Both magnesium and calcium concentrations were high which
may cause magnesium deficiency due to competition between both.
Premontane Rainforest (Valleys / Terraces)
The premontane rainforests stocked on drained sites which occur either on gently sloped
terrain, not necessarily close to rivers, but also on higher terraces where natural disturbances by
landslides or fire are unlikely to occur. No premontane rainforests were found on QuaternaryPleistocene substrate which is too dissected to form wider valleys or broad ridges. In the
premontane rainforests trees grew tallest (> 30 m) with a distinct top canopy layer and produced
most biomass (238.7 t ha-1) although the number of stems per hectare was relatively low. The top
tree layer abounded in lianas and epiphytes. Leaf size domination shifted from large leaves
(50 - 225 cm 2) in the lowest layer to medium leaf size (4 - 50 cm2) in the top layer. Soils were
moist but always well drained sandy fluvisols or clayey oxisols. Phosphorus pools in premontane
rainforest soils were the highest found among all vegetation units.
3.3.3.2 Summary
Geologic Effects
Geology determined the occurrence of shapumbales which were restricted to Quaternary
sediments (chapter 3.1.3.2). Valleys of sufficient width to provide relatively level ground for tall
vegetation had never formed on Quaternary-Pleistocene substrate in the Río Avisado area. Palm
rich rainforest dominated the narrow valleys instead. Both chamizal types were only found on
plateaus of Quaternary-Holocene origin. Low canopy, dry and impoverished rainforest types
occurred independent of geological preconditions.
Vegetation
Results
75
Topographic Effects
Characteristic topographic positions could be ascribed to all vegetation units formed.
Chamizales were restricted to plateaus. Shapumbales were found on steep upper slopes.
Premontane rainforests occurred on sheltered plains, terraces or gently sloped, wide ridges. Palm
rich rainforests developed in steep ravines or on the bottom of narrow valleys. Low canopy
rainforests occurred on sites of moderate slopes which were mainly found towards the foot of
slopes but could also be found on moderately sloped parts further uphill. Especially in the more
dissected hill area dry rainforests covered sites of similar quality as shapumbales. Also on the
lower foothills of the Cordillera Cahuapanas exclusively dry rainforests were found. Impoverished
rainforests were confined mostly to sharp ridges and therefore also found on steeply sloped
terrain neighboring the ridgeline.
Moisture Effects
Vegetation units of badly drained sites were wet chamizales and palm rich rainforests.
Permanently moist but drained were soils of the premontane rainforest. Variable drainage
conditions were assumed for all vegetation units of the slopes. However, higher precipitation
rates at the foothills of the Cordillera Cahuapanas may provide forests there with slightly more
water. Well drained and climatically dry conditions were found for dry chamizales. No distinct
predictions for water regime under shapumbales could be made.
Erratic Effects
A misarrangement caused plot P 42 to be assigned to the palm rich rainforest type. With a
maximum tree height of 37 m and a total biomass of 255 t per hectare it was more likely to belong
to the premontane unit. However, intense hydromorphic properties of the soil may have caused it
to be grouped to the palm rich rainforest. This shows that soil properties may not only have a
disguising effect for biomass as shown in chapter 3.1.3.3 but also may be misleading for the
classification of vegetation types. This error caused a high variability within the palm rich
rainforest unit and led to an overestimate of tree height and biomass values for this unit. A similar
effect was observed for impoverished rainforests. Plot P 40 was recorded on a ridge similar to
most plots within the impoverished rainforest unit. However, it was located on level ground near
the Cordillera Cahuapanas. Geological and stand climatic conditions there were different
compared to the sharp ridges of the upper hill area where the remaining plots of this unit were
described. This plot featured trees and biomass values higher than the average for this unit even
though soil conditions and vegetation structure differed less.
76
Vegetation
Results
250
200
200
150
150
100
100
50
50
-1
Vegetation Units
250
0
0
0
10
20
30
40
50
Dry Chamizal
Wet Chamizal
Impoverished RF
Shapumbal
0
1
2
3
4
5
6
7
Low Canopy RF
Dry Rainforest
8
0
1
2
3
4
5
6
7
Premontane RF
Palm Rich RF
Fig. 3.53: Relation between soil chemistry and biomass for the respective biometry units.
exchangeable aluminum (Al3+) against the biomass values calculated for the stands of each biometry unit.
Variability for the values given is high throughout.
As Fig. 3.53 demonstrates, a trend for increasing biomass development can be observed in
the series of vegetation units from top to bottom of the slopes (left to right). This is not surprising
as moisture regimes may be more favorable and certain sheltering effects from adverse climatic
and disturbance conditions can be expected on lower slopes (low canopy rainforest) and in
valleys (premontane rainforest) allowing a more luxuriant vegetation to develop. Statistical
analysis confirms that the vegetation units can be grouped into three categories by their biomass
stock (cf. One-Way ANOVA in Appendix 9.2.2.4). Observing the phosphorus supply, the trend
follows the biomass values closely with the exception of the palm rich rainforest. In spite of
relatively rich nutrient pools similar to the premontane rainforest, little biomass develops. While
the shortage of nutrients other than P and N may be a possible explanation for the low biomass, it
is likely that the permanently wet soils may prevent an efficient conversion of the nutrients into
biomass. In addition to possible water stress, phosphorus limitation probably inhibits the
production of more biomass in dry rainforests and shapumbales. Aluminum concentration,
although high in many soils, does not seem to affect the biomass growth adversely. An
arrangement of species tolerant to high aluminum concentrations in the premontane rainforest
has been suggested but could not be evaluated from this data.
Spatial Distribution
Results
77
3.4 Spatial Distribution of Vegetation
In order to detect and describe common patterns in the spatial distribution of vegetation in the
study area it was necessary to use remote sensing data (BÖRNER & ZIMMERMANN 2002, READING
et al. 1995). Together with topographic data that had been obtained in the field it was possible to
waive cost intensive satellite images in order to produce an adequately accurate model of the
topographic situation in the study area from aerial photographs.
3.4.1 Image Data
Radar images (RADARSAT (C-Band), and JERS-1 (L-Band) with pixel resolution 100 m)
available from 1996 and 1997 were of insufficient spatial resolution for such small scale
vegetation patterns of the study region (Fig. 3.54). Foreshortening effects caused by terrain with
high relief energy disturb all radar images which are not interferometrically available and
processed. The highly dissected relief of the study area also caused a small scale scatter
variation, which made a radiometric and terrain correction impossible. These effects are
detrimental to a comprehensive set of information as signals of only certain slope aspects can be
detected and evaluated depending on the image azimuth.
Cloud cover near the foothills of the Cordillera Cahuapanas is nearly continuous due to the
condensation of moist air masses ascending at this mountain range. Thus, all examined
LANDSATTM scenes (acquisition dates from 1996 until 1999) were covered by low clouds over the
study area (Fig. 3.55). The only useful data source were panchromatic, stereographic aerial
photographs of the study region, available from October 1992 (scale 1 : 80 000). However, the
acquisition date of 1992 did not reflect the current situation of clearings and vegetation
disturbance patterns (fire, landslides) nine years later.
3.4.2 Topographic Data
A topographic map (1-IGN J631 1459 (12-i), scale 1 : 100 000) was available from the
Instituto Geográfico Nacional in Lima. The data provided by this map were insufficient since the
50 m contour lines did not resolve the hills in the area (Fig. 3.56). GPS reference points were
therefore taken in the field and later used to reference two stereoscopic panchromatic aerial
photographs (Fig. 3.57; IGN 1992). These photographs served as basis to compute a digital
elevation map (DEM) with a surface elevation resolution of 2 m of the core region of the study
area (cf. also Fig. 9.6 - Fig. 9.8 in Appendix 9.3).
78
Results
3.4.3 Spatial Arrangement and Analysis
The analysis of the vegetation in the study area also considered connections to edaphic
factors such as underlying geology and topographical effects. In addition to the existing
Fig. 3.54: JERS-1 radar image (L-Band) of the study
area showing coarse spatial resolution
and intense foreshortening due to ridges.
Fig. 3.55: LANDSAT-7
(Enhanced
Thematic
Mapper) image of the study area
showing high cloud cover.
The contours of the DEM and plot locations are indicated.
Pixels are re-sampled from 18 m to 100 m resolution.
The contours of the DEM and plot locations are indicated.
Channels 7, 4 & 1 (RGB) are used, 30 m pixel resolution.
Source: LANDSAT-7 ETM+, July 11, 1999 (Path8, Row64)
Fig. 3.56: Topographic map (1 : 100 000) of the study
area showing low vertical resolution.
Fig. 3.57: Panchromatic aerial photograph of the
study area from October 17, 1992.
The 20 m contours of the DEM and plot locations are
superimposed.
The outlines of geological formations, 25 m contours of the
DEM and plot locations are superimposed.
Qh-al/fl (Quaternary-Holocene), Qp-al (QuaternaryPleistocene), Ki-c (early Cretaceous), Js-s (late Jurassic)
Source: JERS-1 Tile 116, October 1995, courtesy of GLOBAL
RAINFOREST MAPPING PROJECT, NASDA/MITI
Source: IGN Lima, Topographical Map 1 : 100 000
Cajamarca, 1-IGN J631 1459 (12-i)
Nueva
Source: IGN Lima; Roll 17: Strip 335: No. 64
Results
79
geological data the digital elevation map (DEM) of the visible surface of the terrain was used to
assess relative position of the plots within the landscape as well as relief properties (Fig. 3.58).
The three-dimensional view of the study area (Fig. 3.58, lower image) reflects the landforms
described in the previous chapters. The foreground is dominated by almost parallel trending
ridges (crests are shaded in yellow) of the dissected upper hill area. These ridges eventually
~ 1 : 85 000
~ 1 : 55 000
Fig. 3.58: Spatial distribution of vegetation types as classified by combined analysis.
Plots are colored by corresponding vegetation type and pictured (below) on the surface of a 3-dimensional
aspect of the study area to demonstrate topographical position and (above) on the same view expressing the slope
situation for each plot. Vertical distances are exaggerated three-fold. Plots P01 - P03 are not pictured.
80
Results
Tab. 3.14: Slope distribution in the study area.
Slopes were derived from a 2 300 ha sample DEM in
five distinct classes with a grid resolution of 10 m.
Slope
Area
°
ha
%
0- 2
2- 7
7-15
15-25
> 25
65
387
838
785
226
3
17
36
34
10
total
2302
100
extend to the oblong plateau of the studied chamizales. To the left of this plateau the wider valley
of a tributary to the Río Avisado unfolds outward of the section. The landscape in the background
is characterized by wider and gentler ridges before these rise at the far end to form the Cordillera
Cahuapanas. The gradual rise of the projected terrain surface becomes obvious in this aspect.
Additionally, a map of slope classes was computed from the DEM in order to determine the
slope situations which indicate the susceptibility to erosion or landslides in the study area
(Fig. 3.58). Classes were chosen to distinguish level sites (slope 0 - 2°) which are primarily
characteristic for the plateaus where chamizales are found but also broad ridges and alluvial
valley bottoms (of which the upper canopy surface was mapped) featured such level areas
occasionally. These areas constitute only 3 % of the mapped area. Together with the portion of
gentle slopes (2 - 7°) which concentrate in similar areas these account for 20 % of the digitalized
area. The bulk area of the study side is of sloped terrain. Most prominent are areas of the slope
the classes 7 - 15° and 15 - 25° which cover 36 and 34 % of the total assessed area respectively.
Steep slopes (> 25°) which are most likely susceptible to erosion such as landslides and thus
might be indicative for the occurrence of shapumbales account for 10 % of the area.
a
I
II
III
b
IV
Marked in red are the positions of Gamínedes (I), Camp 2 (“Burnt Camp”, II), Camp 3 (“Chamizal Camp”, III), and Camp 4 (“Cahuapanas Camp” IV).
White arrows indicate the look direction of Fig. 3.60 (a), and Fig. 3.61 (b).
U(
MT
18
S
24:
94
00
93/
68
33
9)
Fig. 3.59: Panoramic view across the upper hill area towards the East (center) from a pre-summit of the Cerro Tambo.
Indicated in yellow are sites of vast shapumbal cover (primarily after fire) , chamizal plateaus , recent landslides , and the Pico de Cahuapanas (1 841 m a.s.l.)
.
U(
MT
18
S
26:
02
81
93/
65
72
5)
Fig. 3.60: Panoramic view across the study area northward towards the Cordillera Cahuapanas in the Río Avisado watershed.
Recognizable is the semi-deciduous vegetation covering the upper slopes and ridges of the upper hill region (dry and impoverished rainforest) in the center.
To the left, vast shapumbal areas can be seen. The Pico de Cahuapanas is to the right.
in the background left, the broad valleys covered by premontane rainforest
in the foreground,
81
Clearly visible are the plateaus covered by chamizales
and abandoned agricultural clearings on the right.
U(
MT
18
S
26:
35
75
93/
73
27
0)
Results
Fig. 3.61: Black and white panoramic view across the upper hill area southward from the foothills of the Cordillera Cahuapanas.
82
Results
3.4.4
▲
◄
Fig. 3.62: View of a typical slope in the upper hill area with
vegetation of decreasing growth density and height
towards the ridge top.
(UTM 18S: 261034/ 9367396)
Fig. 3.63: View into a valley with the palm Jessenia bataua along the
ravines and on the bottom. (UTM 18S: 260281/ 9365725)
▲
◄
Fig. 3.64: Close view of an upper slope with light green colored
shapumbal ground cover.
(UTM 18S: 261177/ 9368208)
Fig. 3.65: View of a shapumbal slope, easily recognizable with many
bright and shiny stems visible. (UTM 18S: 261846/ 9369502)
▲
◄
Fig. 3.66: Aspect of a wet chamizal with many small and short
stemmed individuals and a group of tall individuals of
Mauritiella peruana.
(UTM 18S: 261692/ 9369297)
Fig. 3.67: Aspect of a dry chamizal with considerably higher
vegetation.
(UTM 18S: 261586/ 9369220)
Results
83
Proposed Mapping Criteria
In this chapter guidelines will be presented for the distinction of vegetation characteristics
based on mapped information.
3.4.4.1 Vegetation Types
Based on characteristics of the vegetation types which were classified in chapter 3.3 the
following criteria were proposed for subsequent mapping of the vegetation cover in the study
area:
•
Premontane Rainforests are found in broad valleys of late Jurassic and early
Cretaceous geology where signature on the aerial photograph is coarsely structured by
large crowns protruding the canopy. This type also includes forests on wide ridges at
> 1 050 m a.s.l. on Quaternary-Holocene substrate which bear a similar signature.
•
Palm Rich Rainforests in the study area are only found on both Quaternary formations.
They stock on relatively narrow depressions of less than 200 m width on slopes < 15°
and in narrow ravines leading down from the ridges on steeper slopes.
•
Low Canopy Rainforests occur on both Quaternary formations as well. Straight slopes
of up to 25° which are not incised by ravines bear this forest type.
•
Dry Rainforests cover all slopes > 15° on late Jurassic and early Cretaceous formations.
•
Impoverished Rainforests are found on all geologic formations on ridges of slopes up to
15°.
•
Shapumbales cannot be mapped from aerial photography as no distinct signature could
be derived which links current field data to the image situation nine years ago.
Indications from site analysis, however, suggested that this vegetation type occurs on
frequently disturbed sites. These may be slopes greater then 25° on the QuaternaryPleistocene formation. Therefore, all areas matching this criterion must be considered
potential shapumbal sites.
•
Dry and wet chamizales cannot be easily distinguished in aerial photos. They occur both
on strictly flat plateaus of Quaternary-Pleistocene origin. Dynamics within these
formations may be high which is suggested by the nine-year-old aerial photograph
which shows two contrasting signatures for currently similar vegetation types. Dry
chamizales exhibit a darker and coarser signature in the photograph compared to wet
chamizales. Wet chamizales are found on strictly flat plateaus of QuaternaryPleistocene origin. They are very light and smooth in signature.
84
Results
3.4.4.2 Biomass
This study finds that above-ground biomass in forests varies little between low canopy, dry
and impoverished rainforests, and between wet and dry chamizales. Therefore, it appears
justifiable to simplify the mapping scheme for biomass to the classes valley, barranco, hill area
rainforests, and chamizal:
•
Valley type forests (premontane rainforests) are found in broad valleys of late Jurassic
and early Cretaceous geology where signature on the aerial photograph is coarsely
structured by large crowns protruding the canopy. This type also includes forests on
wide ridges > 1 050 m a.s.l. on Quaternary-Holocene substrate which bear a similar
signature.
•
Barranco type forests (palm rich rainforests) are only found on both Quaternary
formations occurring in the study area. They stock on relatively narrow depressions of
less than 200 m width on slopes < 15° and in narrow ravines that stretch downwards
from the ridges on steeper slopes.
•
Hill area forests (low canopy, dry and impoverished rainforests) occur on all sloped areas
in the study region that do not belong to the barranco class up to an elevation of
1 200 m a.s.l..
•
Chamizales occur strictly on flat plateaus of Quaternary-Holocene origin.
•
The shapumbal biomass unit can be classified only as a potential formation for visible
landslides or all other sloped areas > 25° on Quaternary-Pleistocene substrate.
Classification Approaches
Discussion
85
4 Discussion
This chapter discusses four hypotheses in order to identify factors that cause the variation
and distribution of forest types in the Río Avisado region. Special attention will be given to the
occurrence of heath forests and brief comments will be made on the environmental implications
for human land use in the area.
4.1 Classification Approaches
To test for differences in soil properties, biomass allocation, and the influence of topography
on the vegetation three different classification approaches were chosen. All field data were
evaluated statistically using Principal Component Analysis and / or Hierarchical Cluster Analysis
(cf. chapter 3). Results of the three classification approaches are compared in Fig. 4.1, where all
45 investigation plots are grouped by the vegetation types reached by the combination of all data
(X-axis). Assignments of each plot to soil classes (A) and biometry classes (B) are plotted along
A
Wet
Chamizal
Dry
Shapum- Impover.
Chamizal
bal
Rainforest
Dry
Rainforest
Low Canopy Palm Rich Premontane
Acidic Sand
Soil Units
Litter
Aluminum / Clay
Carbon Subsoil
P01
P03
P44
P41
P05
P09
P13
P42
P08
P16
P04
P06
P14
P43
P35
P18
P34
P38
P02
P17
P36
P45
P37
P39
P15
P10
P40
P07
P11
P12
P21
P20
P30
P32
P19
P33
P23
P25
P28
P29
P31
P24
P26
P27
P22
Base Cation
Biometric Units
Shrubland
Open Woodland
Dense Forest
Short Forest
Tall Forest
B
Wet
Chamizal
Dry
Shapum- Impover.
Chamizal
bal
Rainforest
Dry
Rainforest
Low Canopy Palm Rich Premontane
Fig. 4.1: Overview of relationships between statistical classification units generated by the soil, biometric and
combined approach.
All plots studied are grouped by the forest types to which they were assigned through statistical classification
combining information from soil, biometric and structure parameters. Units are arranged by topographic position
on slopes from top to bottom.
The upper chart (A) demonstrates the different soil units represented in each vegetation unit (solid triangles). The
lower chart (B) demonstrates the different biometric units in each vegetation unit (solid circles).
86
Discussion
the Y-axis. Results from One-Way ANOVA Analysis of the vegetation types were used to reduce
significant parameters. From this reduced set of parameters a classification scheme was
developed for the forest types of the study area.
4.1.1 Soil Chemistry and Texture
NULL-HYPOTHESIS I
Soil chemistry and soil texture do not vary
in the upper Río Avisado watershed.
Five soil clusters were formed based on parameters expressing soil texture, soil reaction, and
element composition (cf. Fig. 3.3 - Fig. 3.16 in chapter 3.1.2). These clusters were also clearly
separated by statistical analyses post-hoc (cf. ANOVA results; Tab. 9.20 in Appendix 9.2.2.4).
Therefore, hypothesis I must be rejected.
Tropical soils are largely known for their poor nutrient status (RICHARDS 1996). It is a
common belief that the limitation of tropical plant growth by phosphorus (P) affects mainly lowland
rainforest whereas with altitude nitrogen (N) limitation becomes increasingly influential (GRUBB
1989, LAURANCE et al. 1999, SILVER 1994, TANNER et al. 1998, VITOUSEK 1984). Such large scale
generalizations are difficult since N and P conditions in tropical soils are highly variable in terms
of the concentration of their exchangeable fraction as well as in overall supply (CUEVAS & MEDINA
1988). Due to this variability conclusions as to the limiting effect of either N or P can only be
drawn on regional level. TANNER et al. (1998) conducted fertilizer experiments in several tropical
forests where they found montane plant growth to be limited by N only in Venezuela and in Costa
Rica. In Jamaica both P and N were shown to limit plant growth whereas Hawaiian montane
forests were P deficient. Hawaiian soils are almost entirely of volcanic origin and contain high
amounts of allophanes which fix P permanently (HINTERMAIER-ERHARD & ZECH 1997, SANCHEZ
1989). A similar situation could arise in the case of acidic sand soils of the Río Avisado area if
volcanic material was identified in the upper horizons as proposed in a previous investigation by
ONERN (1983).
In tropical montane forests many species have developed physiological adaptations to low
phosphorus levels (SILVER 1994) or reduce growth according to the P supply (CHAPIN 1983). The
efficiency of mycorrhiza in enhancing the P uptake for plants at minute concentrations is pointed
out by BAILLIE et al. (1987), SILVER (1994), and W ILD (1989). Such strategies may explain the
absence of chloroses and necroses as deficiency symptoms in the vegetation of the study area.
In contrast to nitrogen, the exclusive sources for plant-available phosphorus in soils are the
mineralization from organic matter and the weathering of primary minerals if soils are not yet
Discussion
87
deeply weathered and P supplies become inaccessible to plant roots (SILVER 1994). CUEVAS &
MEDINA (1988) and LAURANCE et al. (1999) discuss the reduced P availability due to high
concentrations of aluminum (Al), especially within the range of Al buffering (pH < 5.5; W ILD 1989).
According to their studies, P is not only fixed as aluminum phosphate or adsorbed onto
sesquioxide nodules but the high Al levels also inhibit mycorrhizal activity. Deeply weathered and
dystrophic soils with high Al saturation are characteristic for the Río Avisado area. Plant-available
P was restricted to topsoil horizons with high humus content. This suggests that the P supply in
the soils of the study area is closely linked to organic matter. Confirmation might come from
TIESSEN et al. (1994) who found up to 70 % of phosphorus in soil organic matter (SOM) of the
Venezuelan Rio Negro region. Evaluation of soil P and N status in relation to the actual aboveground biomass (cf. Fig. 3.17 in chapter 3.1.3.3) revealed that nitrogen was sufficiently supplied
with the exception of acidic sand and litter soils and that biomass strongly correlated with the
variation of phosphorus. In the hills of the Río Avisado region, BÖRNER (2000) found a correlation
of low pH and low P levels in soil to small stems which could be confirmed in this study, yet on a
less significant basis.
Nitrogen is biologically fixed from the atmosphere and mineralized from organic matter by
microbial activity. Limitation of plant growth by nitrogen in tropical lowlands has been reported
only in a few instances, e. g. the terra firme forests over geologically very old bedrocks along the
Rio Negro (TIESSEN et al. 1994). The estimation of the actual amount of nitrogen (N) available to
plants by mineralization appears to be an essential parameter besides the determination of total
nitrogen pools. In some cases literature reports considerable discrepancies between the N-pool in
the soil and nitrogen concentration in the vegetation (GRUBB 1989). In these cases the nutrient
supply to plants was much lower than suggested by the soil N-pool. Generally, nitrogen is readily
available under tropical warm and moist climate (VITOUSEK 1984) because favorable conditions
for mineralization exist in terms of soil reaction, soil texture, soil water, and especially
temperature. Soil temperature is one of the limiting factors to N-mineralization at higher
elevations (W ILD 1989, SILVER 1994). Nitrogen-mineralization in lowlands is inhibited in
waterlogged, anaerobic or strongly acidic soils. This situation applies to the base cation and
acidic sand soils of the Río Avisado area. Due to the short period of investigation, mineralization
rates were not assessed. However, parameters which influence mineralization like the C/N ratio
and pH in soils were measured (BAILLIE 1989).
Additionally, the element composition of the organic layers may be of significance for plant
nutrition beacause the majority of nutrient cycling in tropical forest soils takes place in these soil
compartments (CUEVAS & MEDINA 1986, SWIFT & ANDERSON 1989). The exchangeable contents
of the nutrients phosphorus (P), potassium (K), calcium (Ca), and magnesium (Mg) were
determined for the sampled soils using standard methods that had been developed to
characterize the short-term availability of nutrients to crops. Regarding the characterization of the
nutrient situation of tropical forests, W ILD (1989) points out the shortcomings of these methods.
88
Discussion
Especially in forest soils, long-term availability of nutrients may be supplemented by the
weathering of primary minerals and seasonal fluvial deposition (BAILLIE 1989, BAILLIE et al. 1987).
W ILD (1989) proposed an analysis of total nutrient stock and a measurement or estimation of the
rate of mineralization in addition to the analysis of easily exchangeable nutrient contents (see
also BAILLIE 1989). For this purpose a mineralogical analysis of the soil might be useful to
determine total potassium which is considered the most mobile nutrient (CUEVAS & MEDINA 1988)
and thus easily leached in the mostly sandy soils of the study area. TANNER et al. (1989)
emphasize multiple interactions between nutrients resulting in a limitation of plant growth. Various
antagonistic effects exist between cations competing in root uptake processes (BAILLIE 1996,
HINTERMAIER-ERHARD & ZECH 1997). Limitation in the growth rates of a forest stand is not
necessarily caused by a low nutrient pool in the soil or a limited availability but imbalances in the
proportions of the absorbed nutrients may be of significance (LINDER 1995). A crucial role in
vegetation development is ascribed to magnesium (BAILLIE et al. 1987, CUEVAS & MEDINA 1988).
Especially podzols which are represented in the acidic sand and carbon subsoil soils in the Río
Avisado region but also litter soils were low in Mg (SCHACHTSCHABEL et al.1989,
VAN
W AMBEKE
1992). For a better assessment of existing nutrient deficiencies a combined sampling of nutrient
contents in soil and plants is required as well as an estimation of the mineralization of nutrients
and weathering of primary minerals.
BAILLIE (1989) advocates the existence of open nutrient cycles under conditions of good
nutrient supply which is indicated by deeply rooted vegetation. Deepest rooting in the study area
was observed in Al / clay soils. The root systems are generally shallow under vegetation with a
closed nutrient cycle where nearly all nutrients are confined to organic matter and no nearsurface weathering of nutrient rich substrate occurs. In the upper hill region BÖRNER (2000)
observed a concentration of roots in the upper 20 cm of the solum. This was confirmed by the
present study suggesting rather poor lithogenic nutrient supply and closed nutrient cycles,
especially for the acidic sand soils of the Río Avisado region.
It was possible to prove in this study that soils differed significantly within the upper hill region
and adjacent areas (cf. Fig. 4.1). Soil units were in some instances linked to topographic position.
This is the case for acidic sand soils on plateaus, litter soils on steep slopes and base cation soils
in ravines, on lower slopes, and in valleys. But soils especially along the slopes of the upper hill
area vary on a small scale reflecting fine layers of contrasting substrate (BÖRNER 2000). Although
soil units alone were not able to characterize or explain the vegetation distribution some strong
correlations existed.
Discussion
89
4.1.2 Above-Ground Biomass
NULL-HYPOTHESIS II
Above-ground stand biomass is homogeneously distributed
within the upper Río Avisado watershed.
Five biometry clusters were formed based on parameters expressing the allocation of
biomass in terms of stand density and tree dimensions. (cf. Fig. 3.20 - Fig. 3.29 in chapter 3.2.2).
Biomass itself was not directly entered into analysis because it was derived from the data on
stand density and tree dimensions. Nevertheless, all five clusters were clearly separated by
statistical analysis post-hoc (cf. ANOVA results; Tab. 9.21 in Appendix 9.2.2.4). Therefore,
hypothesis II must be rejected.
Biomass estimates for tropical rainforests vary greatly over the tropical belt (HOUGHTON et al.
2001). Many different estimation techniques have been applied leading to different results when
scaled up to regional levels and beyond (CATCHPOLE & W HEELER 1992, HOUGHTON et al. 2001).
Direct comparability of the results remains questionable due to different approaches and local
variation of abiotic factors (FEARNSIDE et al.1993). In this regard, special attention should be paid
to small scale variation of stands within each region. LAURANCE et al. (1999) hold mainly edaphic
factors responsible for such variation on two accounts: a) Biomass on different soils varies due to
altered species composition featuring different growth rates and b) the enhanced growth rates of
a similar floristic set under more favorable soil nutrient status. In the hill region of the Río Avisado
area patterns of highly variable stand biomass were first reported by BÖRNER (2000). Like in this
study, BÖRNER (2000) surveyed her plots by the relascope technique choosing representative
random samples of trees.
Regular techniques of forest inventory fail in low scrub vegetation since most methods
include the stem diameter at a tree height (dbh) of 1.3 m. This height was rarely reached by the
thin stems of this dense vegetation. Therefore, a biomass harvest was conducted for all aboveground parts of woody individuals. Monocotyledonous plants which frequently form a dense
undergrowth as well as mosses or lichen were not included in order to comply with the approach
taken in the forests. Thus biomass values in the shrubland and open woodland units must be
considered to be minimal values.
In the upper hill region of the Río Avisado almost all recorded stands contain more than
1 000 stems per hectare (gbh > 10 cm), the average is 3 411 stems per hectare. The highest
value is 7 519 stems per hectare on a ridge in the upper hill region. These findings are only
slightly higher than in the study by BÖRNER (2000) who reported an average of 2 239 stems with a
maximum of 5 347 stems per hectare for the upper and lower hill areas together. The study area
of DEMPEWOLF (2000) is located to the west of the area considered here. Even higher counts of
90
Discussion
up to 9 108 stems per hectare were found there. Stands with a large number of stems but
moderate stand height were described by TANNER (1980) in the Blue Mountains in Jamaica. As
possible reasons for this a limited supply of the nutrients N, P, K and Ca (cf. also TANNER 1985),
low pH-values or moderate radiance were identified. Water stress as a reason was ruled out by
TANNER (1977) in the Blue Mountains on the basis of long-term soil moisture measurements.
METTE (2001) drew similar conclusions from moisture measurements at the Cerro Tambo near
the study area.
Phosphorus supply is frequently critical in impoverished tropical soils. Contrary to C or N, no
biological fixation or absorption from the atmosphere is possible. The P pool is nourished by the
parent material of the soil (VITOUSEK 1984, W ILD 1989). Positive correlations were found between
the amount of litterfall and P concentrations in the litter of tropical lowland forests which indicated
P limitation rather than N limitation in these forests (VITOUSEK, 1984). At low pH-values of 3.5 - 4.0
and high Al concentrations in the soil solution P forms aluminum phosphate complexes and is
thus immobilized.
In the short and dense forests of the Río Avisado area P limitation is evident (cf. Fig. 3.30 in
chapter 3.2.3.3). This is expressed by relatively low biomass values under conditions of high N
pools but low P concentration in the soils. Better supply of phosphorus combined with similar N
pools in the tall forests yielded significantly higher biomass. Both N and P supply and thus
biomass were low in shrubland and open woodland accounting for the extremely low biomass
there.
There was evidence that other edaphic factors did influence stand structure as well. Besides
a strong correlation between biomass and topsoil P statistical analysis revealed further evidence
for plant growth to be influenced by other nutrients which was already suggested by BAILLIE et al.
(1987) and CUEVAS & MEDINA (1988) (cf. Tab. 3.7 in chapter 3.2.3.3). In this study,
undecomposed litter, low pH values, and wide C/N ratios in the topsoil were negatively correlated
to tree size indicating that mineralization was a strong estimator for plant nutrition (BAILLIE 1989,
LAURANCE et al. 1999, W ILD 1989). Exchangeable concentrations of magnesium and potassium in
the subsoil correlated positively to tree size which also implies the significance of these nutrients
to growth patterns in these forests.
In the investigation by BÖRNER (2000) aluminum concentrations only correlated significantly
with the occurrence of smaller stems but no significant relationship was found between Al
concentration and biomass. This is supported by the indifferent reaction of biomass to varying
aluminum concentrations in this study. Fig. 3.30 in chapter 3.2.3.3 shows no clear trend because
the highest biomass was produced by tall forests under fairly aluminum rich conditions whereas
low aluminum saturation in the open woodlands yielded only about 10 % of the biomass from tall
forests which surprisingly show high P levels together with high Al concentrations. Aluminum
saturation of soils seems not to be an important limiting factor to plant growth.
Discussion
91
It was demonstrated that biomass production and soil conditions were correlated. It can be
concluded that results from biometric measurements lacked a distinct grouping for vegetation
types. For example, in the open woodland unit and the short forest unit several structurally
different stands were combined. However, stands of different nature and growth strategies
achieved similar pools of overall biomass. Thus, biomass alone is not suitable as a criterion to
describe vegetation types with fairly low forest biomass below 130 t per hectare. Although
assignment of plots to biometric units did not yield uniform groups in terms of vegetation types it
was possible to demonstrate and explain general trends in growth.
In conclusion, it is proven that above-ground stand biomass is not homogeneously distributed
within the Río Avisado area.
4.1.3 Topography
NULL-HYPOTHESIS III
There are no significant differences in vegetation patterns
between different topographic situations.
Both classification approaches could not characterize vegetation patterns in the Río Avisado
at the desired resolution. In both cases units were synthesized which still combined vegetation
types that were evidently different in their stand structure. In order to account for these
differences a third classification approach was chosen to attain a more complete separation of the
studied vegetation into robust units. For this purpose all parameters for soil and biometry were
combined with recorded parameters describing stand structure (e. g. epiphyte and liana
abundance, layering of the canopy, leaf sizes). Eight vegetation units were formed which were
successfully linked to topographic positions by evaluating field observations on topography and a
digital elevation map (cf. Fig. 3.58 in chapter 3.4.3). Therefore, hypothesis III must be rejected.
On a large regional scale, GRUBB (1989) describes altitudinal gradients in stand structure. He
finds forests decreasing in stand height and being composed of more and slimmer individuals
with increasing altitude. Stands of low stature in the moist tropical lowlands are generally ascribed
to nutrient deficiencies. Similarly, TANNER et al. (1998) argue that, besides regular stands in
tropical lowlands of heights 20 - 40 m, stunted forests shorter than 10 m occur on acidic soils at all
altitudes. These stands suffer exclusively from nitrogen and / or phosphorus deficiency.
Topography is held accountable for disturbance hazards (GRUBB 1989), for the difference in soil
water and soil nutrient status (TIESSEN et al. 1994), and consequently for floristic and biomass
variation (KLINGE & HERRERA 1983, LAURANCE et al. 1999).
92
Discussion
In this chapter the implications of topography on the development and distribution of soils, on
biomass and ultimately on vegetation will be discussed for all closed canopy forest formations of
the Río Avisado region. Since the topographic and edaphic conditions of the wet and dry
chamizales differed completely from all other sites the occurrence of these heath forest types will
be discussed separately later in this chapter.
Soil
Most authors consider the altitude of 1 000 m a.s.l. to be within the transitional zone between
tropical lowland and montane vegetation (CAVELIER 1996, FUKAREK et al. 1995, GENTRY 1992,
HUECK 1966, INRENA 1996, READING et al. 1995, W HITMORE 1991, YOUNG 1992). Therefore, no
assumptions could be made whether lowland conditions expressed by P limitation (N readily
available due to good mineralization but too acidic for P to be plant-available) or montane
conditions expressed by N limitation (due to inhibited mineralization at lower temperatures) would
prevail (cf. GRUBB 1989, LAURANCE et al. 1999, VITOUSEK 1984). DEMPEWOLF (2000) found an
accumulation of organic material in soils with increasing elevation on a vertical 600 m transect
west of the study area suggesting an increasing limitation by nitrogen due to slow mineralization
(CÔUTEAUX et al. 2002).
The altitudinal gradient within the Río Avisado area of this study was negligible. Thus, focus
was on small scale variation of topography and soils. In Fig. 4.1 (A) (page 85, above) the
vegetation types are arranged according to their topographic position in the hill area. The
topographic gradient from upslope positions downwards is seen in the arrangement of vegetation
types from left to right. Soils found under shapumbales, palm rich rainforests and premontane
rainforests were fairly consistent whereas several different soil types were combined in the
vegetation units on slopes (impoverished, dry, and low canopy rainforest). This may be due to the
fact that geological substrate varies greatly and drives variation in texture of the parent material in
the upper hill region. This was confirmed by BÖRNER (2000) who examined a soil catena on a
slope gradient of 70 m in the lower hill area. The nutrient dynamics of the upper hill area seem to
resemble lowland conditions (cf. chapter 3.3) since the plant growth is obviously limited by
phosphorus rather than by nitrogen (cf. Fig. 3.17, Fig. 3.30 & Fig. 3.53). An exception to this are
frequently waterlogged soils of the palm rich rainforests where both exchangeable P
concentrations in the mineral soil and total N concentrations are comparable with premontane
rainforests but biomass of the palm rich type is less than half of that of the premontane rainforest
(cf. Fig. 3.53). The high P concentration in comparison with all other forest types of the upper hill
region can be explained by the flushing of nutrients from upslope (GRUBB 1989) and by the
constantly high water content which causes reducing soil conditions. TIESSEN et al. (1994) claim
that such reducing soil conditions suppress the accretion of sesquioxide cementations which are
Discussion
93
known to have a high P-sorption capacity due to their large surface area. Thus, the exchangeable
P fraction in such water saturated soils may be high whereas the anaerobic conditions inhibit any
microbial mineralization of the organic matter that accounts for the high Ntot values. It can be
concluded that palm rich rainforests are the only forest type in the study area suffering from N
limitation.
No direct symptoms of aluminum toxicity were found. Most biomass was developed by the
premontane rainforests under conditions of equally high aluminum saturation as in any other
forest types of the upper hill region. Investigations by BÖRNER (2000) showed that several plant
species occurred in the natural forests of the study area that are known to be extremely tolerant
to high levels of aluminum. METTE (2001) found extremely high Al concentrations in leaves of
Melastomataceae which can be shed in order to dispose of such excess toxic elements. This is a
known strategy to cope with toxicity (GRUBB 1989).
Biomass
In the previous paragraph, a close relation was recognized between topography and soil
fertility. LAURANCE (1999) reports a close link between nutrients and biomass. In his study in the
Amazon lowlands, soil fertility accounts for one third of the biomass variation. He found positive
correlations between biomass and total nitrogen, base cations and clay whereas aluminum and
sand influenced biomass negatively. Of his conclusions, only the positive effect of base cations
on biomass can be affirmed by this study.
In Fig. 4.1 (B) (page 85, above) biometric units follow the decreasing slope gradient quite
strictly which is another direct indication for a link between biomass and topography. ONe
exception is the biometric units assigned to the impoverished rainforest, which are predominantly
associated with ridge positions. There, a return from the dense to the short forest is observed. In
this case, however, nomenclature is misleading since individuals of the impoverished rainforest
tend to be slightly larger than in the forest types further downslope (cf. Fig. 3.26 and Fig. 3.27 in
chapter 3.2.2). This is in accordance with observations by GRUBB (1989) who stated that stands
on ridges are higher than on slopes especially for unstable soils which are found particularly in
the upper hill region.
According to values for biomass the vegetation units can be merged into three groups (cf.
Fig. 3.29 in chapter 3.2.2): a) productive stands with biomasses above 200 tons per hectare
(impoverished rainforests), b) all stands on impoverished soils in the hill region with biomasses
slightly above 100 t ha-1, and c) unproductive stands such as the chamizal and shapumbal
vegetations with biomasses below 50 t ha-1.
94
Discussion
Tab. 4.1: Biomass, stand height, and basal area of tropical rainforests.
Findings from this study are compared in respect to their stand biometry to potentially corresponding rainforests
types from literature to a) Premontane rainforests, b) impoverished rainforests, and c) heath forests.
Forest type
Location
Elevation
Above-ground Stand
biomass
Height
-1
m a.s.l.
t ha
Basal
area
m
m² ha
Source
-1
a) PREMONTANE RAINFORESTS
Terra firme forest
Rio Xingu, Brazil
-
254
-
-
HEINSDIJK (1958) 1)
Tropical forest
Sao Miguel de Guama, Brazil
-
253
-
-
GLERUM & SMIT (1962) 1)
Dry monsoon-rain forest
Chiang Mai Province, Thailand
500
267
< 26
35.4
Lower montane rain forest
Luquillo Natl. Forest, Puerto Rico
510
146.5-246.7
30
36.0-41.0
Tropical moist forest
Panama
-
269
-
-
Tropical seasonal evergreen forest
Magdalena Valley, Colombia
-
252
-
-
Lower montane rain forest
Sierra de Chamá, Guatemala
900
457-499
< 40
46.0
Tropical montane wet forest
India
-
457
-
-
Productive broadleaf forest
Tropical America
OGAWA et al. (1965)
OVINGTON et al. (1970) 1)
GOLLEY (1975) 1)
FÖLSTER et al. (1976) 1)
KUNKEL-W ESTPHAL et al. (1979) 5)
RAI (1981) 2)
-
155
-
-
BROWN & LUGO (1984)
Tropical evergreen submontane forest Brazil
-
268
-
-
KAUFFMAN et al. (1995) 1)
Tropical montane wet forest
San Carlos de Rio Negro, Venezuela
-
314
-
-
DELANEY et al. (1997) 1)
Tropical lower montane moist forest
-
346
-
-
DELANEY et al. (1997) 1)
Valley forest
Alto Mayo, Peru
830-980
240
< 30
40.0
DEMPEWOLF (2000)
Lower slope rainforest
Alto Mayo, Peru
850-1000
192
< 30
35.2
BÖRNER (2000)
Premontane rainforest
Alto Mayo, Peru
920-1100
239
< 36
30.1
DIETZ (2002)
-
170
< 11
-
KLINGE & MEDINA (1979)
-
170
-
-
DEANGELIS et al. (1981) 2)
b) IMPOVERISHED RAINFORESTS
Low Amazon Caatinga
Tropical premontane moist forest
-
Unproductive broadleaf forest
Tropical America
-
89
-
-
BROWN & LUGO (1984)
Premonate tropical forest
Bajo Urubamba, Peru
-
124
-
-
ONERN (1990) 1)
Tropical evergreen forest
San José de Cabitu, Beni, Bolivia
-
166
-
-
KUDRENECKY (1993) 1)
Dense tropical moist forest
Amazonia, Brazil
-
226
-
-
HIGUCHI et al. (1994) 1)
Dense tropical moist forest
Amazonia, Brazil
-
185
-
-
HIGUCHI et al. (1994) 1)
Hill forest
Alto Mayo, Peru
870-1300
202
> 25
36.0
DEMPEWOLF (2000)
Upper slope hill forest
Alto Mayo, Peru
850-1000
138
< 20
33.2
BÖRNER (2000)
Semi-deciduous hill forest
Alto Mayo, Peru
900-1000
107
< 20
27.5
DIETZ (2002)
-
-
1.8-4.5
-
c) HEATH FORESTS
Muri heathland
Coastal Guyana
FANSHAWE (1952) 6)
Heath forest
Kampuchea, Cambodia
-
145
-
-
HOZUMI et al. (1969) 4)
Swamp forest
Kampuchea, Cambodia
-
11
-
-
HOZUMI et al. (1969) 4)
Padang heathland
Bako Natl. Park, Sarawak, Borneo
-
-
0.5-7
-
SPECHT & W ORMERSLEY (1979)
Campina
Rio Negro, Brazil
-
-
1.5-15
-
Bana forests
-
-
-
20-31
BRÜNIG et al. (1979) 3)
Open forest
Tropical America
-
33-77
-
-
BROWN & LUGO (1984)
Heath forest
Alto Mayo, Peru
900-1400
5-162
< 18
5-35
DEMPEWOLF (2000)
Heath forest
Alto Mayo, Peru
1200-1400
7-50
3.5-9
15-40
METTE (2001)
Chamizal
Alto Mayo, Peru
980
7-32
2-8
4-13
DIETZ (2002)
1)
from HOUGHTON et al. (2001)
2)
from BROWN & LUGO (1984)
3)
from BRÜNIG (1983)
4)
from GOLLEY (1983)
5)
from CANNELL (1982)
6)
from COOPER (1979)
Above-ground biomass estimates for premontane rainforests after OGAWA et al. (1965) were
comparable to values from literature listed in Tab. 4.1. Values for unproductive semi-deciduous
forests in wet tropical regions are rare. Average above-ground biomasses for the impoverished
upper hill region are relatively moderate compared to other studies. They agree best with
biomasses found by ONERN (1990, cited in HOUGHTON et al. 2001) at the lower Urubamba River
Discussion
95
of Peru, where forests were also classified as “moist forest of the upper hills” by INRENA (1996).
Little information is available on above-ground biomass of heath forests. Compared with values
provided by literature stand characteristics of chamizales are comparable but biomass is low
relative to other tropical heath forests. However, above-ground biomass of the chamizal
vegetation is within the range reported previously for heath forests in the study area (DEMPEWOLF
2000, METTE 2001).
Vegetation
Studies in the dipterocarp rainforests of Sarawak led BAILLIE et al. (1987) to the conclusion
that the profundity of soils and topography determined the diversity and physiognomy of the
vegetation. In the study area, it was possible to arrange the vegetation units shown in Fig. 4.1
(page 85, above) along a downslope gradient of their topographical position. The best match
between vegetation type and topography was seen for the chamizales on plateaus and will be
discussed later in this chapter.
Shapumbales constitute an exceptional and presumably dynamic vegetation formation found
frequently in the upper hill area. A “Gleichenia thicket ecotone” is reported for warm temperate
zones at 2 400 m a.s.l. in Sumatra (READING et al. 1995) but Sticherus remotus has also been
observed as a dominant colonizing species in the Peruvian lowlands (PINO GARAY 2002, pers.
communication). The occurrence of these fern dominated woodlands is difficult to ascribe to
edaphic site conditions since this vegetation type seems closely linked to natural disturbances
which are difficult to predict. Potential shapumbal sites could be only identified by instable
geology and steep upper slopes (cf. Fig. 3.58 in chapter 3.4.3). Tree canopy of this vegetation
type as seen from vertical projection is still dense and obstructs clear identification of these
stands by optical remote sensing techniques. On the ground, shapumbales can be recognized by
the high amount of stems visible from a lateral perspective.
The pronounced dry season between June and August apparently leads to the formation of
semi-deciduous forest types in the study area. Investigation began within these months when all
forest types with the exception of the palm rich forests and the chamizales clearly showed
deciduous individuals in their stands. This was especially pronounced in the dry rainforest
indicating a temporary water shortage within the area during the dry period which was confirmed
by METTE (2001) who found that forest stands on the Cerro Tambo exhaust all soil water during
the dry season.
Impoverished rainforests are primarily found on ridges where soils are poorest in nutrients.
These sites are frequently characterized by deeply weathered podzols with a low water retention
capacity due to their sandy texture. GRUBB (1989) mentions the susceptibility of soils to drought in
96
Discussion
such topographic positions. Dry rainforests occur on upper slopes where asandy substrate still
prevails but some input of moisture and nutrients is provided by lateral flow. Species richness and
the density of slender stems increases under these conditions (GRUBB 1989, LAURANCE et al.
1999). Low canopy rainforests on the lower slopes suffer from inherently poor soil conditions but
profit from nutrients flushed in from upslope (TIESSEN et al. 1994) which can be better retained in
soils containing higher amounts of clay. Their stature of rich understoreys up to 10 m high and a
single canopy at 20 - 25 m resembles the description by READING et al. (1995) for lower montane
rainforests. Palm rich rainforests are found in ravines (barrancos) or tight valleys of the upper hill
area on permanently wet soils or along small streams. Theses stands were dominated by the
palm Jessenia bataua which is characteristic of seasonally waterlogged soils of mixed tropical
rainforest on hills (KLINGE & HERRERA 1983). Premontane rainforests are restricted to wide
valleys or gentle slopes on rather clayey but well drained soils which BAILLIE et al. (1987)
considers to be prerequisites for the formation of productive lowland rainforest. Stand structural
properties combine characteristics of tropical evergreen rainforest and tropical lower montane
rainforest in the sense of W HITMORE (1991).
In the forest type map of Peru (INRENA 1995) the entire upper hill region is classified as
“bosque humedo de las colinas altas” (moist forest on upper hills). This forest type is not very
common covering only 1.4 % of the territory of Peru. It is described to be stratified clearly up to a
height of 35 m on moderately dissected terrain. Based on this study this classification is
questionable. A list of characteristic timber species for this forest type is provided by INRENA
(1996) but floristic data are lacking in this study. However, the underlying relief must be described
as highly dissected (cf. Fig. 3.59 in chapter 3.4.3). Clear strata were not found (cf. Fig. 3.51 in
chapter 3.3.2.3) and stand heights of 35 m were never reached within the upper hill region. The
same forest type occurs also along the lower Río Urubamba in the south of Peru. Investigations
at the lower Río Urubamba reported a similarly low biomass stock as found in the Río Avisado
area (cf. Tab. 4.1).
Discussion
97
Tab. 4.2: Occurrence and classification of tropical heath forests.
Findings from this study are compared in height to sclerophyllous closed scrub formations and heathlands of
different nomenclature which are described from other tropical regions of Asia and South America.
Borneo
Guyanas
Rio Negro /
Amazon River
Peruvian
Amazon
Alto Mayo
Excessively
drained
Padang scrub
Muri scrub
Low campina / bana
Varillal
Dry chamizal
2-7m
2-3m
3 - 10 m
10 m
< 10 m
Waterlogged
Padang heathland
Bush islands
Low campina / bana
Chamizal
Wet chamizal
<2m
< 1.5 m
1.5 - 3 m
<3m
< 4.5 m
SPECHT &
W ORMERSLEY (1979)
COOPER (1979)
KLINGE & MEDINA
(1979)
Formation
Soil Moisture
Sclerophyllous
closed scrub
Heathland
References
RUOKOLAINEN & DIETZ (2002)
TUOMISTO (1993)
Heath Forest
The most conspicuous and locally best defined vegetation types in the Río Avisado area
were the open and stunted scrubs and woodlands of the wet and dry chamizales. Similar
formations have been described under the general name of heath forests in literature. Due to
their unique soil and vegetation properties these vegetation types will be discussed here as a
whole.
Heath forests occur as locally well defined vegetation formations throughout the humid
tropical lowlands although local names and scientific nomenclature varies (Tab. 4.2). Other
reported sites of heath forests closest to the study region are located in the lowlands of eastern
Peru (PRANCE 1989, RUOKOLAINEN & TUOMISTO 1993).
This formation is commonly characterized as a xeromorphic or sclerophyllous scrub
community (COOPER 1979, RICHARDS 1996, SPECHT & W ORMERSLEY 1979) of perhumid tropical
lowlands (W HITMORE 1995). According to SPECHT & W ORMERSLEY (1979) the occurrence of
tropical heath forests has a distinct gap between lowlands and subalpine regions; only in Borneo
they can also be found at elevations between 1 000 - 2 000 m a.s.l. (RICHARDS 1995). All heath
forest types are exclusively found on slightly elevated sites featuring extremely nutrient poor,
leached, white sandy soils which are drained by black water rivers (BRÜNIG 1983, COOPER 1979,
METTE 2001, PRANCE 1989, SPECHT & W ORMERSLEY 1979, W HITMORE 1995). Those soils form
either on mostly Pliocene / Pleistocene alluvial or litoral sand deposits or are bleached in situ as
ground water podzols resulting in substrates of almost pure quartzitic sand (BRÜNIG 1983,
SARMIENTO & MONASTERIO 1975, SPECHT & W ORMERSLEY 1979). Heath forests developing in
seasonal savannas or peat swamps on mesa-like outcrops of sandstone, where a hardpan at the
place of an ancient water table impedes drainage, are well described for South America by
SARMIENTO & MONASTERIO (1975) and for Asia by BRÜNIG (1983). Although it seems questionable
98
Discussion
to assume lowland conditions for the study region all the above characteristics of heath forests
correspond to observations made during this study.
Most authors consider heath forests as pedobiomes or rather peinobiomes in the sense of
W ALTER & BRECKLE (1984) since extremely adverse edaphic conditions are held responsible for
the reduced plant growth (FERRI 1960 in KLINGE & MEDINA 1979, KLINGE et al. 1990). Soils are
described to be either of coarse texture with a low water retention capacity or mostly waterlogged
due to a shallow duripan (BRÜNIG 1983, KLINGE & MEDINA 1979, RICHARDS 1996, SARMIENTO &
MONASTERIO 1979). Heath forest soils are commonly strongly acidic reaching toxic levels of
hydrogen ions (W HITMORE 1989) below the range of aluminum buffering (pH < 4) and are
inherently low in sesquioxides (RICHARDS 1996) although SPECHT & W ORMERSLEY (1979) report
fairly high iron concentrations which were confirmed in the chamizales of the study area. Macronutrients such as N and P are low in all white sand soils (CUEVAS & MEDINA 1986, 1988, SPECHT
& W ORMERSLEY 1979, VITOUSEK 1984, cf. also Tab. 3.4 in chapter 3.1.3.3). Due to the extreme
scarcity of nutrients special strategies for phosphorus conservation have been reported in such
plants (VITOUSEK 1984). Symbiontic relationships with termites as mentioned by W HITMORE
(1991) were also observed in the study area on waterlogged sites. RICHARDS (1996) points out
that precious nutrients stored in evergreen leaf tissue are protected from herbivory by the
production of toxic phenols which in turn requires an efficient mycorrhizal activity since microbial
mineralization of leaf litter is reduced by these substances (W HITMORE 1989). Slow mineralization
in the study area is indicated by the build-up of peat-like organic layers with wide C/N ratios (cf.
Fig. 3.36 in chapter 3.3.2.1).
Although water stress is not considered to be crucial in the development of campinas (KLINGE
& MEDINA 1979) or in the heath forests on the slopes of the Cerro Tambo (METTE & ZIMMERMANN
2002) short dry spells of 6 - 8 days or even a short dry season may occur. According to BRÜNIG
(1983), water stress under either poor or excessive drainage is the key factor for the development
of heath forest vegetation. Both situations are found on the white sand plateaus in the Río
Avisado area. Waterlogged conditions are found in the center whereas soils toward the rim of the
plateaus drain deeply.
Indications for nutrient and moisture conditions which mutually enhance their restrictive
effects on plant growth can be found in the chamizales of the study area. It was observed that
rooting depth in these soils was extremely shallow and concentrated in the organic horizons due
to nutrient scarcity in the mineral soil (cf. chapter 4.1.1). Waterlogged conditions prevailed in wet
chamizales due to impeded drainage by a shallow hardpan less than 40 cm below the mineral
soil surface. However, roots were observed reaching not further than 10 cm into the mineral
horizon possibly due to an extremely low pH. In this case, a minimal drop of the ground water
level may have a significant impact on the water supply for the vegetation. Therefore, edaphically
induced restrictions of the root system may limit the water uptake from such soils.
Discussion
99
Tab. 4.3: Floristic elements linking the chamizales of the Alto Mayo with other tropical heath forests.
Plant families, genera and / or species listed are taken from literature where they were considered to be
characteristic for certain heath forest types. All floristic elements listed were also encountered on the dry and / or
wet chamizales in the study area during an incomplete, preliminary floristic inventory.
Family
Genus
Species
Heath Forest Location
Reference
Evergreen sclerophyllous
all tropics
KLINGE & MEDINA (1979),
RICHARDS (1996),
Ericaceae
all tropics
PRANCE (1989),
Lycopodiaceae
Lycopodium
all tropics
Sphagnaceae
Sphagnum
all tropics
Cyperaceae
Carex
ssp.
all tropics
PRANCE (1989), RICHARDS (1996)
Humiriaceae
Humiria
balsamifera
South America
PRANCE (1989), RICHARDS (1996)
Chamizal, Peruvian Amazon
RUOKOLAINEN & TUOMISTO (1993)
Chamizal, Peruvian Amazon
Varillal, Peruvian Amazon
ssp.
Campina / Bana, Rio Negro
Muri, Guyana
PRANCE (1989), RICHARDS (1996),
ssp.
Campina, Brazil
sp.
Campina, Brazil
Campina, Brazil
Padang, Borneo
PRANCE (1989),
Bana, Venezuela
RICHARDS (1996)
Bana, Venezuela
PRANCE (1989)
Melastomataceae Graffenrieda sp.
Arecaceae
Mauritiella
sp.
Clusiaceae
Clusiaceae
Clusia
Melastomataceae Miconia
Nyctaginaceae
Neea
Orchidaceae
Arecaceae
Mauritia
Cladonia
sp.
Heathland vegetation is genuinely evergreen (RICHARDS 1996, SPECHT & W ORMERSLEY 1979)
but BRÜNIG (1969, cited in W HITMORE 1991) found for eight heath forest species in Borneo that
heath forest species are as sensitive to desiccation due to water shortage as rainforest species.
Thus, given a low water supply, a number of strategies must be sought to minimize evaporation.
Many examples are given in literature which applied to the chamizales of the study region as well.
Among such strategies are the avoidance of direct insolation by nearly vertical positions of the
leaves (BRÜNIG 1983, KLINGE & MEDINA 1979, PRANCE 1989, SPECHT & W ORMERSLEY 1979,
W HITMORE 1991) or avoidance of heating of their shiny and light colored leaves (W HITMORE
1991). According to W HITMORE (1991) “other adaptations reduce water loss from the heath forest
as a whole. These are the clustering of leaves, the clustering of leafy twigs into dense subcrowns,
and perhaps also the very uniform forest canopy top surface which by its smoothness reduces
turbulent mixing of air”. A smooth and even canopy structure of heath forests can be easily
recognized from the air (RICHARDS 1996, SPECHT & W ORMERSLEY 1979). This is particularly true
for the chamizal sites of the study area which were identifiable by their smooth texture in aerial
photographs (see Fig. 3.61 in chapter 3.4.3 and Fig. 9.6 in Appendix 9.3.1).
100 Discussion
Palms were reported to occur in the chamizales of the upper Amazon river (RUOKOLAINEN &
TUOMISTO 1993) and in Neotropical peat swamps (SARMIENTO & MONASTERIO 1975). Palms were
observed on patches within the wet chamizal (see Fig. 3.66 in chapter 3.4.3) which suggest
locally permanent water saturation of the soil. Thus, differences in soil moisture created small
scale patterns within the stand. These patterns could be reinforced due to a higher resistance of
such permanently wet sites to fires which occasionally affect heath forests (COOPER 1979, KLINGE
& MEDINA 1979, SPECHT & W ORMERSLEY 1979). Edaphic conditions are also held accountable for
the gradual transition from well drained sites supporting higher vegetation to more stunted stands
on waterlogged soils which is also reflected in the floristic composition of the higher and lower
formations (cf. Tab. 4.2 and Tab. 4.3). This is, however, in contrast to observations by METTE
(2001) who documented sharp transitions between heath forests and bordering rainforests on the
slopes of the Cerro Tambo west of the study area. Heath forests of similar floristics occur there
on slopes of white sandstone instead of sandy deposits. Due to the inclination of the terrain
waterlogged sites do not exist there. Instead of differences in moisture regime of the soils only
different depths were encountered under heath and rainforest on the Cerro Tambo. It may be
hypothesized that heath forests on such slopes occupy shallow initial soils of former landslide
locations. This hypothesis should be tested by further investigations.
Conclusion
The combined analysis and classification approach resolved the vegetation patterns better
than all previously performed approaches. It was modified and refined post-hoc according to
structural and topographical aspects. Erratic effects were observed indicating that it was difficult
to find common characteristics for the entire study region. Two plots which were assigned to
supposedly inappropriate units were recorded in the region of the Cordillera Cahuapanas where
geological and microclimatic conditions differ from the upper hill region (METTE 2001). The
influence of soil parameters in the presented classification approach appears to be high since
questionable assignments could be reproduced best if considering edaphic factors. Fertilizer
experiments as well as further investigations on foliar nutrients (VITOUSEK 1984) and the
mineralization dynamics are required to verify these assumptions. Although indications were
found that plant growth is limited by phosphorus deficiency on most sites, soils appear to be only
one factor behind the development of different vegetation types. Topographic and consequently
stand climatic factors are likely to influence the distribution of all vegetation types.
Quantitative approaches such as soil and biomass assessment alone are not able to produce
a robust distinction between vegetation types (cf. Fig. 4.1). Nevertheless, for evaluations on a
larger scale, biomass as a descriptive parameter may serve well to characterize an entire
ecosystem such as the upper hill area although stand height, at higher resolution than presented,
would be a much better estimator to describe the observed vegetation patterns. Stand biomass
Discussion 101
per se does not permit a non-ambiguous separation of vegetation types (e. g. low canopy, dry
and impoverished rainforests, or dry chamizal and shapumbal) which pursue different strategies
of biomass allocation within the stand but yield similar overall biomass.
4.1.4 Simplified Classification
NULL-HYPOTHESIS IV
Separation of the Río Avisado forest types requires comprehensive
information on both: edaphic site conditions and stand structure.
A vegetation classification scheme was developed excluding of soil parameters. The idea
was to create a straight forward guideline for assessing the vegetation in the upper Río Avisado
area based on parameters which are easy to determine in the field. Seven parameters were
chosen from the dataset which showed statistically most significant differences between the
groups that were separated in each step (cf. ANOVA results; Tab. 9.22 in Appendix 9.2.2.4).
Fig. 4.2 depicts the decision pattern for this classification scheme. Since it was shown that a
robust classification could be achieved under the exclusion of soil parameters, hypothesis IV
must be rejected.
Initially, it is differentiated between open and closed canopy formations to account for the low
biomass and low tree formations such as shapumbal and chamizales. As a measure of crown
cover the proportion of small leaves in the lowest layer is used. It was the parameter which most
significantly separated both groups. A high proportion of small leaves (≤ 4 cm 2) in the shrub and
lower canopy layers (> 10 %) reflects the lack of a protective shield by a dense canopy layer.
Stands of the open canopy group are eventually examined for the number of woody individuals
shorter than 2 m. High rates (> 3 000 stems ha-1) indicate shrub land or wet chamizal. Low stand
densities of up to 3 000 stems ha-1 are characteristic for woodland types. Distinction is made
between dry chamizal on moderately sloped terrain (≤ 5°) and shapumbal on steep slopes (> 5°).
Stands of the closed canopy group are distinguished based on their number of tall trees (> 20 m).
If the number of tall trees exceeds 50 individuals per hectare tall stands of alluvial plains and
terraces will be identified as premontane rainforest vegetation. All stands with less than 50 tall
individuals per hectare are attributed to sloped terrain of the hill areas or the foothills of the
Cordillera Cahuapanas. In the next step the number of palms is evaluated. High palm abundance
(> 100 individuals ha-1) indicates moist conditions and therefore palm rich rainforest vegetation. In
all remaining stands the number of thick stems greater than 70 cm in diameter is assessed.
Several stems of such dimensions (> 4 per hectare) point to better and more stable growth
conditions which can be found in low canopy rainforest. The final distinction between dry and
102 Discussion
impoverished rainforest is made based on the proportion of small leaves (≤ 4 cm 2) in the top tree
layer which was the only statistically valid parameter to distinguish between these units. High
proportions of leaves of this class (> 20 %) indicate dry rainforest.
It turns out that all parameters applied for separation can be interpreted to be functional for
each corresponding unit or super-ordinate group. Thus, it proved possible to classify the
vegetation of the study area with statistical significance by simple means of field observation.
Vegetation of the Upper Río Avisado Region
High
(> 10 %)
Proportion of
Small Leaves (≤ 4 cm2 )
in Lowest Tree / Shrub Layer
Low
(≤ 10 %)
Open Canopy Formation
Closed Canopy Formation
Low
Number of Short Woody Individuals
(≤ 2 m)
High
Low
(> 3 000 ha- 1)
(≤ 3 000 ha-1 )
(≤ 50 ha-1)
Number of Tall Trees (> 20 m)
Short Stands of Dissected Hill Region
High
Number of Palms
(> 100 ha- 1)
Low (≤ 100 ha -1)
Shrub Type
Woodland Type
Dry Stands on Slopes
Terrain Inclination
Number of Thick Stems (Ø > 70 cm)
Low
High
(≤ 5°)
(> 5°)
Low (≤ 4 ha-1 )
High
(> 50 ha -1)
Tall Stands
of Alluvial
Plains and
Terraces
Moist Stands
on Slopes
High
(> 4 ha-1)
Proportion of
Small Leaves (≤ 4 cm2 )
in Top Tree Layer
Wet
Chamizal
Dry
Chamizal
Shapumbal
Low
High
(≤ 20 %)
(> 20 %)
Impoverished
Dry
Low Canopy
Palm Rich
Rainforest
Premontane
Rainforest
Fig. 4.2: Proposed key for ground based classification of vegetation units in the study area.
The units were produced by combined analysis of all assessed parameters. The classification scheme is based
primarily on significantly different parameters of above-ground biometric, structural and topographic site
characteristics.
Ecological Evaluation
Discussion
103
4.2 Ecological Evaluation
Soils in the highly dissected upper hill region at the Río Avisado are inherently poor in
nutrients. On the dystric soils of the region, closed nutrient cycles exist which are particularly
sensitive to disturbance (SARMIENTO & MONASTERIO 1975). Given the predominantly steep relief
which affects soil water conditions and landslide hazards, these soils are not sustainable for any
land use (TIESSEN et al. 1994). Since the loss of vegetation leads to rapid soil degradation when
the scarce nutrients are quickly mineralized from organic material under higher soil temperature
due to direct insolation. The lack of root mats and the predominantly sandy texture of the soils
yield a low water retention capacity, increased leaching of nutrients and soil erosion are the result
on the steep relief (see Fig. 2.10 in chapter 2.1.6). Secondary growth would be forced to reestablish a structure of intact nutrient cycles (TIESSEN et al. 1994). Successful re-growth of
vegetation after land use is unlikely on the soils in the upper hill region. This area is limited for
agricultural use per se due to their infertility, strong acidity, poor water holding capacity, and high
aluminum content (LAURANCE et al. 1999,
VAN
W AMBEKE 1992). High aluminum saturation was
found to be influential on the phosphorus dynamics and would render fertilization ineffective
unless soil pH and texture were improved simultaneously. Such soil treatment is not economically
feasible under the topographic and edaphic preconditions in the upper Río Avisado area.
Aluminum toxicity obviously does not directly harm the natural vegetation of the upper hill area
since species occurring naturally on these sites are adapted to high aluminum levels (METTE
2001). However, crop plants quickly suffer from nutrient deficiency (GRUBB 1989). LAURANCE et
al. (1999) point out that the damage of pasture use of such infertile soils is both immediate and
irreversible. He proposes that the natural forest is the only sustainable plant cover under such
edaphic conditions.
The forests of the upper hill region do not possess biomass stocks for suitably economic
timber exploitation. Large trees are very rare and accessibility to this remote area is difficult. Also,
forest growth rates and productivity are too low to support forestry (ZIMMERMANN et al. 2001).
Forest stands producing considerable timber for extraction are only found in wider valleys near
the flanks of the Cordillera Cahuapanas. However, the same problems of accessibility also apply
there. The forests of the study area are classified as “bosques humedos de las colinas altas”.
This forest type is suggested to be subject to ecological protection (INRENA 1996).
Protection is particularly recommended for the chamizal areas of the Río Tioyacu and Río
Avisado watersheds. W HITMORE (1989) concluded that heath forests are truly fragile ecosystems
which cannot sustain agriculture. Also SARMIENTO & MONASTERIO (1975) claim such
hydromorphic soils to be unfavorable for plant growth in general and consider them the “poorest
soils of tropical America”. PRANCE (1989) found a rate of endemism greater than 50 % in
Venezuelan muri vegetation. If similar rates can be confirmed for chamizales, the need for the
conservation of this unique and fragile vegetation form will be evident in order to the protect the
biodiversity in the region.
104 Discussion
Outlook
4.3 Outlook
The short investigation period available for this study required a concisely recorded data set.
In some instances during the evaluation, a richer dataset would have been desirable. Some
results of this study remain tentative at this point and will require confirmation by further
investigation. However, based on the results of this study several approaches to further
investigation seem promising in order to enhance the dataset for the intended ecological
evaluation:
•
A soil inventory of the Río Avisado region has been conducted by BÖRNER (2000) and by
the present study. However, data on nutrient cycling are still lacking. METTE (2001) looked
at nutrient distribution in biomass, but his investigation was restricted to a small area
within the studied region and included only two contrasting vegetation types.
•
In order to fully understand the nutrient budgets of the study area and to confirm
indications on nutrient limitations, representative measurements for below-ground
biomass, mineralization rates, nutrient storage in biomass, and leaf area should be
considered.
•
The generation of a high-resolution digital elevation model from stereoscopic aerial
photography proved successful. A GIS based overlay of derived topographical data in
combination with geology and readily available data on biomass and water budgets
should be implemented as a proper tool to characterize, survey and model the ecological
implications of anthropogenic influences on the studied watersheds.
•
Further hypotheses can now be formed on different succession dynamics in the
landscape to explain the occurrence of heath forests on the slopes of the Cerro Tambo or
the shapumbal vegetation in the upper hill region. Attempts to characterize vegetation
dynamics should be supported by specific investigations and inventories of the floristic
diversity of the region.
Summary
Discussion 105
5 Summary
•
Eight forest types were classified in the Río Avisado and Río Tioyacu region using
Principal Component Analysis and subsequent Hierarchical Cluster Analysis for data
describing soil, biomass and vegetation structure. Most forest types could be ascribed to
certain topographic features.
•
Soils of the upper hill area are generally strongly acidic and poor in nutrients. The
vegetation of the upper hill area tends to be limited by phosphorus deficiency. Links
between topographic situation and soil properties exist, but soils alone are not suitable to
completely characterize the variation and distribution of forest types.
•
Forest biomass stocks within the upper hill region of the Río Avisado and on the
piedmonts of the Cordillera Cahuapanas are remarkably low compared to rates published
for productive lowland and premontane rainforests.
•
A simple regression formula based on height and girth was established for the prediction
of biomass for the palm Jessenia bataua (Arecaceae).
•
The highest stands of low statured heath forests reported in tropical America, besides the
paramo vegetation, were found at 1 000 m a.s.l., and a biomass harvest was conducted
to establish the living above-ground biomass.
•
Due to their extremely fragile nutrient cycles and a presumably high rate of floristic
endemism, patches of heath forest vegetation (chamizales) are recommended for
protection for permanent conservation.
•
Natural disturbances (landslides and / or fires) occur and probably trigger a conspicuously
open woodland (shapumbales) with a dense undergrowth of ferns (predominantly
Sticherus remotus and Gleichenella pectinata, both Gleicheniaceae).
•
The soils and vegetation of the Río Avisado region are not suitable for economically and
ecologically sustainable land use.
Literature
107
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112 Glossary
7 Glossary
Anthropogenic Influence
Cluster
Is defined hereby as direct and intentional actions by
humans in order to change the characteristics of their
environment such as burning of vegetation or felling of
trees as well as measures for redirecting or creating
water bodies (e. g. for irrigation). Not included are
secondary effects such as atmospheric element
immissions or changed floristic compositions due to
the lack of extinct browsers or predators.
In statistical terms a cluster is referred to as an
association of cases, grouped by a Hierarchical
Cluster Analysis as long as the represented cases
have not been interpreted further.
Barranco
Spanish for ravine. In local vernacular language the
term applies to steep and narrow ravines which are
characteristic for the highly dissected upper hill area of
the Río Avisado watershed and constitute a portion of
the slopes leading form the ridges down toward the
valleys. Barrancos often display small creeks of
moister soil conditions than the plain slopes in the
vicinity.
Biomass
If not indicated otherwise biomass in this study
always refers to the dry above-ground biomass of all
woody species > 1.3 m in height. This includes foliage
weight, branch weight and stem weight but excludes
root biomass.
Biometry
Refers to all quantifiable parameters of living plant
individuals. In this case these are height, stem diameter
at breast height (dbh) and growth density as well as
derived parameters such as biomass. No estimation
(without prior statistical evaluation in case of empirical
allometries) is involved.
Carretera Marginal
Spanish for Marginal Road. Built in the 1970’s this
road leads across the Andes massif connecting the
northern Pacific Coast of Peru ( Costa) with the
Amazon Basin ( Selva).
Case
For statistical analysis a case is referred to as the
complementary term to variable in a data matrix. In
this study a case always represents a plot.
Chamizal (pl. chamizales)
Locally referred to a shrub-like vegetation type with
short, stunted trees and xeromorphic / peinomorphic
leaves. The term is derived from a cluster of
representative species (e. g. Brachyotum sp.,
Melastomatacea; Bertieria sp., Rubiaceae and
Cavendishia sp., Ericaceae) all uniformly named
“Chamizo” ( Heath).
Comunidad Nativa
Largely
self-governing
community
of
indigenous tribes on granted territory. Nowadays,
the territory is marked and protected against
intruding settlers. All indigenous tribes of the
study area belong to the Aguaruna group.
Cordillera (Occidental / Oriental)
Spanish term describing any given mountain
range which needs to be specified further. The
Cordillera Occidental and Cordillera Oriental
refer to the western and eastern range of the
Peruvian Andes, respectively, which constitute the
Sierra.
The study site is located at the piedmonts of the
Cordillera Cahuapanas which forms the
easternmost range of the Andes in northern Peru
south of the Solimões.
Costa
Spanish for coast. It describes the arid, narrow
coastal plains of an average width of 60 km which
stretch along the Pacific Ocean.
Disturbance
Complementary to anthropogenic influence
the term disturbance is applied to natural abiotic
effects destroying vegetation such as natural fires,
floods, storms, earthquakes and landslides.
Herbivoral damage is not included.
Factor
In statistical terms a factor is referred to as a
synthetic and abstract variable extracted by a
Principal Component Analysis.
Forest
The arrangement of woody individuals > 1.3 m
in height (mostly trees but also tree ferns and
palms) on an area of > 0.1 ha. This does also
include only shrub-like vegetation with
occasionally emergent trees.
Group
In this study a group is defined as a cluster
which has already been interpreted for distinctive
properties between each other.
Glossary
113
Heath
Shapumbal (pl. shapumbales)
This refers to a stunted, shrub-like sclerophyllous
vegetation type commonly described for nutrient poor
(white) sandy soils of the humid tropics (COOPER
1979, SPECHT 1979a, SPECHT & WORMERSLEY 1979,
RUOKOLAINEN & TUOMISTO 1993). Heath forests have
also been described in the study area on sloped terrain
by DEMPEWOLF (2000) and METTE (2001). Heath
forests in this study are comparable to the latter in
terms of floristic composition and general substrate but
occur on level terrain. For distinction, these shall be
termed chamizales.
Locally refers to badlands exclusively covered
by ferns. Ferns of the first successional stages
following cultivation are generally Pteridium
aquilinum. In this study the term shapumbal
specifically applies to a vegetation type with very
open canopy and a dense fern thicket of Sticherus
remotus and Gleichenella pectinata, Gleicheniaceae, of considerable height (≤ 2 m).
Layer
This refers to a subjectively chosen unit. It describes
one vegetation height zone with its upper boundary
characterized by an obvious accumulation of
individuals of similar height. An herb layer, a shrub
layer, and a maximum of three tree layers were
described at each plot.
Parameter
A parameter is one particular property of the
investigated entity (e. g. soil pH, tree height, liana
abundance) which is used to describe the variation
between sites.
Plot
Generally, in this work a plot does not refer to a
graphic chart but rather describes a particular
investigation site which was chosen in order to
represent a certain type of forest stands.
Selva (Alta / Baja)
Spanish for forest. This term is used to describe the
eastern zones of Peru which potentially support
tropical rainforest. This region is subdivided into two
biomes: The Selva Alta (Upper Rainforest), which
covers the Subandean Belt and the Selva Baja
(Lowland Rainforest) in the Amazon Basin.
Sierra
Spanish term locally referring to the Andean
highlands of Peru between the Cordillera
Occidental and the Cordillera Oriental.
Stand Characters
For reasons of rapid assessment an assortment of
conspicuous characteristics among woody plants
were chosen which were assumed to be indicative
of certain stand properties such as microclimate,
soil moisture regime, and nutrient status.
Therefore, the abundance of the stand characters
palms, tree ferns, standing dead wood and shrubs
(≤ 1.3 m) was assessed separately.
Structure
According
understood
arrangement
includes the
characters.
to BARKMAN (1979) structure is
as the horizontal and vertical
of vegetation. In this case it also
differentiation into different stand
Swamp
A pedological unit with nearly constant water
logging or inundation of the mineral and organic
soil with an ombrogenic water regime.
Unit
In this case, a unit (e. g. soil unit) refers to a
group of plots which have already been
characterized in respect of their common
properties and which have been labeled
accordingly.
Settler
Non-indigenous farmer who arrived after 1950 and
who cultivates recently cleared, formerly pristine forest
areas.
Variable
A variable represents the complete set of all
values of a parameter over all cases (= plots)
as used in statistical processes.
114 Abbreviations
8 Abbreviations
flame Atomic-Absorption Spectroscopy
FEARS
Forest Ecology and Remote Sensing
Group
Aluminum (cation)
Fig.
Figure
above sea level
fl.
fluvial
approx. approximately
g
gram
AVHRR Advanced Very High Resolution
Radiometer
gbh
girth at breast height (1.3 m)
GIS
Geographic Information System
GTZ
Gesellschaft für technische
Zusammenarbeit
h
height / hour
AAS
Al
(3+)
a.s.l.
BITÖK
Bayreuther Institut für terrestrische
Ökosystemforschung
BMELF Bundesministerium für Ernährung,
Landwirtschaft und Forsten
(+)
BP
Before Present
BS
Base Saturation
C
Celsius
C(tot)
Carbon (total)
(2+)
Ca
Calcium (cation)
cbh
circumference at breast height
(1.3 m)
CEC(eff) Cation Exchange Capacity
(effective)
cf.
confer
cm
centimeter
cmol(c)
centimol (of cations)
conc.
concentration
dbh
diameter at breast height (1.3 m)
DEFORPAM Desarrollo Forestal
Participativo en la Región del Alto
Mayo
DEM
Digital Elevation Map / Model
DIAM
Desarrollo Integral Alto Mayo
dm
3
cubic decimeter
E
East
Ed(s).
Editor(s)
e. g.
for example
ENSO
El Niño Southern Oscillation
Eq.
Equation
et al.
and others
ETM
Enhanced Thematic Mapper
FAO
Food and Agricultural Organization
(3+)
Fe
H
hydrogen (cation)
H20
water
ha
hectare
hPa
hectoPascal
ICP-AES
Inductively-Coupled Plasma-Atomic
Emission Spectroscopy
i. e.
that means
IGN
Instituto Geográfico Nacional
INADE
Instituto Nacional de Desarrollo
INGEMMET Instituto Geológico Minero y
Metalúrgico
INRENA
Instituto Nacional de Recursos
Naturales
JERS-1
Japanese Earth Remote Sensing
Satellite
K
Kelvin
(+)
K
Potassium (cation)
KA4
Kartieranleitung (AG BODEN 1996)
kg
kilogram
km
km
kilometer
2
KMO
KAISER-MEYER-OLKIN
L
liter
LGM
Last Glacial Maximum
m
meter
m
2
square meter
max
maximum value
mg
milligram
(2+)
Iron (cation)
square kilometer
Mg
Magnesium (cation)
min
minimum value
Abbreviations
min
minute
sp.
Species
Mio
million
SST
Sea Surface Temperature
mm
millimeter
stdev
standard deviation
Mn
Manganese (cation)
t
metric ton
Modif.
modified
Tab.
Table
MSA
measure of Sampling Adequacy
tot
total
N
North
UTM
Universal Transverse Mercator Grid
System
vs.
versus
W
West
WGS
World Geodetic System
WZP
Winkelzählprobe (angle count plot
sampling)
x
multiplied by
µg
microgram
π
pi
Ø
diameter
#
number
(2+)
N(tot)
(+)
Nitrogen (total)
Na
Sodium (cation)
NaHCO3
Sodium bicarbonate
n.d.
no data
NH4NO3
Ammonium nitrate
no.
number
NOAA
National Oceanic and
Atmospheric Administration
ÖBG
Ökologisch Botanischer Garten
ONERN
Oficina Nacional de Evaluación
de Recursos Naturales
P(Olsen)
Phosphorus (plant-available)
%
percent
PEAM
Proyecto Especial Alto Mayo
&
and
ppm
parts per million
<
less than
Q.
Quaternary
=
equal to
RF
rainforest
>
greater than
S
South
°
degree
s.n.m.
sobre nivel del mar
2-D
two dimensional
SOM
Soil Organic Matter
3-D
three dimensional
115
116 Appendix
Contents
9 Appendix
Table of Contents
9.1 Equations ..........................................................................................................117
9.1.1
9.1.2
9.1.3
9.1.4
9.1.5
9.1.6
Barometric Elevation Equation (STULL 1995) ..................................117
Biomass Equations (OGAWA et al. 1965) ..........................................117
Biomass Equation developed for Jessenia bataua ........................118
Upscaling Equation for Stem Abundance after BITTERLICH (1984) 118
Slope Correction Equation for Enhanced Distance Measurement118
t-Value after BACKHAUS et al. (1996)..................................................119
9.2 Statistical Evaluation......................................................................................120
9.2.1
Soil Analysis .......................................................................................120
9.2.1.1
Principal Component Analysis of Soil Data ......................................120
9.2.1.2
Hierarchical Cluster Analysis of Soil Data ........................................120
9.2.1.3
t-Values of Soil Data .........................................................................122
9.2.1.4
Texture Key.......................................................................................123
9.2.2
Combined Analysis ............................................................................124
9.2.2.1
Principal Component Analysis of Combined Data ...........................124
9.2.2.2
Hierarchical Cluster Analysis of Combined Data .............................124
9.2.2.3
t-Values for Combined Data .............................................................126
9.2.2.4
Results from One-Way ANOVA Analysis for all Approaches...........129
9.2.3
Summarized Data ...............................................................................130
9.2.3.1
Biometry Approach ...........................................................................130
9.2.3.2
Combined Approach .........................................................................131
9.3 Maps.................................................................................................................. 132
9.3.1
9.3.2
Aerial Photography ............................................................................132
Digital Elevation Map .........................................................................133
9.4 Plot Documentation ........................................................................................134
9.4.1
9.4.2
9.4.3
Plot Locations.....................................................................................134
Plot Assignments to Soil, Biomass, and Vegetation Units............135
Photographic Documentation of Soil and Vegetation....................136
9.5 Recorded Data .................................................................................................160
9.5.1
9.5.2
Soil Data ..............................................................................................160
Structure Data.....................................................................................168
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Master Thesis Geoecology _Appendix - Ökologisch

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